Apparatus and method for resource allocation prediction and modeling, and resource acquisition offer generation, adjustment and approval

ABSTRACT

An apparatus, method, and computer program product are provided for the improved and automatic prediction and modeling of one or more channels and relevant conditions through which resources may be directed to users in an environment where resource demand, utility, and perceived value vary over time. Some example implementations employ predictive, machine-learning modeling to facilitate the use of multiple disparate and unrelated data sets to extrapolate and otherwise predict the future needs for certain resources and identify the channels and conditions that may be employed to meet such future needs. An apparatus, method, system, and computer program product are provided for improved generating, adjusting, and/or facilitating approval of a resource offer set. Some example implementations employ one or more predictive models.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Non-Provisional patentapplication Ser. No. 16/416,883 entitled “APPARATUS AND METHOD FORRESOURCE ALLOCATION PREDICTION AND MODELING, AND RESOURCE ACQUISITIONOFFER GENERATION, ADJUSTMENT AND APPROVAL” filed May 20, 2019, whichclaims priority to and the benefit of U.S. Provisional PatentApplication No. 62/673,325, filed May 18, 2018, the contents of each areincorporated herein by reference in its entirety.

TECHNICAL FIELD

An example embodiment relates generally to the use of machine-learning,predictive models to implement the efficient allocation oftime-sensitive resources. Example implementations are particularlydirected to systems, methods, and apparatuses for predicting andmodeling future demand for time-sensitive, depreciating objects inresource-constrained environments. Additional or alternative exampleembodiments relate to improved generation a resource offer set, and/orimproved visualization and display of such resource offer set foranalysis, adjustment, and approval.

BACKGROUND

Many of today's network environments are dynamicallyresource-constrained, at least in the sense that the need for resources,and the nature of the needed resources, can change rapidly andsignificantly over time and geography. Some of the technical challengesthat hinder the effective and efficient allocation of resources in suchenvironments are compounded in situations where the supply, utility,and/or value of the needed resources changes over time. Additionally, inthis regard, acquisition of resources for a particular time and/orgeography can change significantly. Technical challenges in datacompilation, analysis, visualization, and manipulation associated withconventional systems hinder efficient resource acquisition planning. Theinventors of the invention disclosed herein have identified these andother technical challenges, and developed the solutions described andotherwise referenced herein.

BRIEF SUMMARY

An apparatus, computer program product, and method are thereforeprovided in accordance with an example embodiment in order permit theefficient determining of one or more channels and/or related conditionsthrough which a particular resource set may be effectively distributed.In this regard, the method, apparatus and computer program product of anexample embodiment provide for the creation of predicted channel andcondition data set that can be stored within a renderable object andotherwise presented to a user via an interface of a client device.

Moreover, the method, apparatus, and computer program product of anexample embodiment provide for use of the machine learning model inconnection with the determination and retrieval of a predicted channeland condition data set determined based at least in part on context dataassociated with a particular resource set to be distributed at a time inthe future.

In an example embodiment, an apparatus is provided, the apparatuscomprising a processor and a memory, the memory comprising instructionsthat configure the apparatus to: receive a request data object from aclient device associated with a user; extract, from the message requestdata object, a request data set, wherein the request data set isassociated with a first set of resources; receive a first context dataobject, wherein the first context data object is associated with one ormore resource distribution channels; retrieve a predicted channel andcondition data set, wherein retrieving the predicted channel andcondition data set comprises applying the request data set and the firstcontext data object to a first model; and generate a control signalcausing a renderable object comprising the predicted channel andcondition data set to be displayed on a user interface of the clientdevice associated with the user.

In another example embodiment, a computer program product is provided,the computer program product comprising at least one non-transitorycomputer-readable storage medium having computer-executable program codeinstructions stored therein, the computer-executable program codeinstructions comprising program code instructions configured to: receivea request data object from a client device associated with a user;extract, from the message request data object, a request data set,wherein the request data set is associated with a first set ofresources; receive a first context data object, wherein the firstcontext data object is associated with one or more resource distributionchannels; retrieve a predicted channel and condition data set, whereinretrieving the predicted channel and condition data set comprisesapplying the request data set and the first context data object to afirst model; and generate a control signal causing a renderable objectcomprising the predicted channel and condition data set to be displayedon a user interface of the client device associated with the user.

In another example embodiment, a method for determining a predictedfuture demand for resources in a dynamic environment is provided, themethod comprising: receiving a request data object from a client deviceassociated with a user; extracting, from the message request dataobject, a request data set, wherein the request data set is associatedwith a first set of resources; receiving a first context data object,wherein the first context data object is associated with one or moreresource distribution channels; retrieving a predicted channel andcondition data set, wherein retrieving the predicted channel andcondition data set comprises applying the request data set and the firstcontext data object to a first model; and generating a control signalcausing a renderable object comprising the predicted channel andcondition data set to be displayed on a user interface of the clientdevice associated with the user.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described certain embodiments of the present disclosure ingeneral terms, reference will now be made to the accompanying drawings,which are not necessarily drawn to scale, and wherein:

FIG. 1 illustrates an example system within which some embodiments ofthe present disclosure may operate;

FIG. 2 illustrates a block diagram of an example device for implementinga prediction system using special-purpose circuitry in accordance withsome embodiments of the present disclosure;

FIG. 3 illustrates a block diagram depicting a functional overview of asystem in accordance with some embodiments of the present disclosure;

FIG. 4 illustrates a data flow model in accordance with some embodimentsof the present disclosure;

FIG. 5 illustrates a block diagram depicting a functional overview ofanother aspect of a system in accordance with some embodiments of thepresent disclosure;

FIG. 6 illustrates a flowchart describing example operations forgenerating resource allocations based on predicted conditions inaccordance with some embodiments of the present disclosure;

FIG. 7 illustrates a flowchart describing example operations forgenerating resource allocations based on predicted conditions inaccordance with some embodiments of the present disclosure;

FIG. 8 illustrates another example system within which some embodimentsof the present disclosure may operate;

FIG. 9 illustrates a block diagram of an example apparatus forimplementing a resource offer generation system using special-purposecircuitry in accordance with some embodiments of the present disclosure;

FIG. 10 illustrates a data flow diagram depicting steps for generatingan optimal resource offer set via a resource offer generation system inaccordance with some embodiments of the present disclosure;

FIG. 11 illustrates a data flow diagram depicting steps for renderingand/or adjusting a resource offer set, submitting the adjusted resourceoffer set for approval, and approving or rejecting the adjusted resourceoffer set, in accordance with some embodiments of the presentdisclosure;

FIG. 12A illustrates a flowchart depicting operational blocks in anexample process for generating a resource offer set, updating theresource offer set to create an adjusted resource offer set, andreceiving an offer status indicator for the adjusted resource offer set,in accordance with example embodiments of the present disclosure;

FIG. 12B illustrates a flowchart depicting operational blocks in anexample process for generating a trusted resource characteristic dataset from one or more untrusted third-party resource characteristic datasets and a distributed resource characteristic set, in accordance withexample embodiments of the present disclosure;

FIG. 13 illustrates an example analysis interface accessible via adashboard, specifically offer adjustment interface in accordance withexample embodiments of the present disclosure;

FIG. 14 illustrates another example analysis interface accessible via adashboard, specifically an offer approval interface in accordance withexample embodiments of the present disclosure; and

FIG. 15 illustrates another example analysis interface accessible via adashboard, specifically a market comparison interface in accordance withexample embodiments of the present disclosure.

DETAILED DESCRIPTION

Some embodiments of the present disclosure will now be described morefully herein with reference to the accompanying drawings, in which some,but not all, embodiments of the invention are shown. Indeed, variousembodiments of the invention may be embodied in many different forms andshould not be construed as limited to the embodiments set forth herein;rather, these embodiments are provided so that this disclosure willsatisfy applicable legal requirements. Like reference numerals refer tolike elements throughout.

Overview

Various embodiments of the present disclosure are directed to improvedapparatuses, methods, and computer readable media for predicting anddetermining an optimized allocation of resources in environments whereresource demand, availability, utility, and/or value are dynamic. Bymodeling and predicting resource requirements, example implementationsof embodiments of the invention are able to more rapidly and efficientlydirect resources (which may be subject to depreciation, spoiling, and/orother dynamic changes in utility or value) to channels in which suchresources may be optimally deployed. One environment recognized by theinventors where resource demand, availability, utility, and value areeach dynamic is a market environment involving the acquisition andresale of used mobile devices. In such an environment, the demand for aparticular mobile device varies with time and may vary widely withgeography, such that one mobile device may be in higher demand in onelocation at a given time compared to another location at the same time,or the same location at a different time. Moreover, in such anenvironment, the supply of a given mobile device may vary based on anumber of factors, while the user's requirements (such as on therequired functionality of a mobile device) and the perceived value of aparticular mobile device, may each vary independently with time. Inparticular, since the value of a particular mobile device tends to trenddownward over time, delay in the allocation of a particular mobiledevice to a particular distribution channel tends to increase thelikelihood that the used mobile device will become wasted throughobsolescence, perceived lack of value, and/or other factors.

The inventors of the embodiments of the disclosure herein haverecognized that one of the key factors in efficiently meeting demandsfor particularized mobile devices in a secondary market environment isthe ability to predict and model user demand and perceived device value.Conventional approaches tend to react to existing conditions in theenvironment, rather than predicting future conditions. As a result,decisions to deploy resources into particular channeled tend to incur insatisfying user needs and demands. Moreover, under reactive approaches,delays are often injected into the process of acquiring the potentiallydesired devices and directing them to the users seeking such devices.Particularly in situations where devices tend to become more obsoleteand less valuable over time, delays in the allocation of devices canresult in the waste of devices that were directed to particular channelsbased on past conditions that cease to be relevant to the existingmarket conditions at the time when the resources are introduced into agiven channel (the used mobile devices in this environment, for example)and a decrease in the value that can be realized from such devices.

As recognized by the inventors of the disclosure herein, the technicalchallenges associated with predicting and modeling user demand andperceived device value are compounded by a wide range of informationocclusion factors. In the case of mobile devices, one of the informationocclusion factors includes the wide range of similar, but potentiallynon-identical, devices in the market. For example, many mobile devicemanufacturers apply different identification numbers or other indicatorson mobile devices based on the mobile network, retailer, cosmeticfeatures, market, and/or other aspect associated with the original saleof the mobile device. For example, the identification number used toidentify a mobile device that was originally sold from a retail outletassociated with one mobile network provider may differ from theidentification number of a mobile device that was originally directed toa retail outlet associated with another mobile network provider,notwithstanding the fact that the two devices may have identicalfeatures and function equally well in a broad range of networks. In someenvironments, the number of device identifiers may number in the tens orhundreds of thousands.

The information that may be used to predict and model user demand andperceived device value may be further occluded by the high volume ofunscaled and/or otherwise non-uniform data associated with each deviceand/or device identification number. For example, a predictive modelthat accurately and reliably identifies channels to which certain mobiledevices should be directed to meet user demand at a given time may use arange of publicly and privately available data sets, including but notlimited to resource disposition data, seasonality information, salesinformation (in business-to-business and/or business-to-customercontexts, for example), mobile device attribute information, marketdata, device claims data (such as information regarding insuranceclaims, warranty and/or other repair claims, or the like, for example),other macroeconomic indicators, equity information, and/or social mediadata. Since many of these data sets are mutually independent, therelevant components of such data sets may need to be extracted,normalized, scaled, and/or otherwise conditioned to allow for the use ofsuch information in a predictive model.

In addition to the technical challenges imposed by the volume,complexity, and variability of the multiple data sets used in connectionwith the predictive model, the inventors of the invention describedherein have also recognized technical challenges imposed by theconditions of a given environment (such as the capacity of any givenchannel to accept and distribute resources effectively, the existingresources available to be distributed, actions of external actors, andthe like), along with the speed at which such conditions change withinthe technical environment. In particular, the inventors have recognizedthat the delays inherent in reactive systems often result ininefficiencies and waste associated with resource allocations that areincongruent with changed and/or shifting conditions in the givenenvironment.

To address these, and other technical challenges associated withallocating dynamically variable resources under rapidly changingenvironmental conditions, users associated with requests for allocationsof resources to channels able to efficiently distribute such resourcesmay be able to interact with a resource allocation prediction systemthat uses a predictive, machine learning model. Through the use of amachine learning model, the system is able to identify, generate, and/orotherwise provide resource allocation guidance based on the contextualinformation associated with the environment within which the resourcesare to be distributed. In contexts involving the distribution of usedmobile devices in a market environment, the system may draw on a widerange of information sources that can be supplied to the machinelearning model to allow for the predicting and modeling of marketconditions to identify the channels in which to allocate particularquantities and types of devices at a given time. Moreover, through theapplication of a decay curve and other aspects of the predictive model,changes in market conditions, resource demand, and other relevantfactors can be predicted, allowing for resource allocations that aremore time-aligned with the conditions at a given time than thoseavailable from conventional reactive approaches.

For example, in contexts where existing inventories of used mobiledevices are to be distributed in an efficient manner, the system mayaccess and process data sets that provide context and/or otherinformation about one or mobile devices and/or the channels throughwhich such devices may be disposed, such as existing asset distributioninformation, historical sales information, competitive pricinginformation, other market information, device attribute information,device performance information (such as insurance claims data associatedwith one or more mobile device models, device use and device status datathat may be acquired through self-service and/or customer serviceplatforms and/or interfaces, or the like, for example), and/or otherpublicly and/or privately available data sets associated with a givenmobile device, channel, and/or environment. The system may also accessand process information associated with additional factors that mayimpact the conditions within a given environment. For example, inaddition to and/or separately from any of the categories listed above,data indicative of seasonal and/or other time-based factors,macroeconomic conditions, social media data, and/or other information(such as manufacturer actions, plans, and/or statements, for example)may be used. The system may also access and process other informationsources, including but not limited to feedback information generated bythe system, decay curve information, training data and the like for usein connection with the machine learning model. Consequently, through theuse of acquirable data, information developed through the use of themodel, and data describing aspects of a mobile device and/orenvironment, one or more channels for distribution of resources (such asused mobile devices, for example) can be identified and selected basedon predicted conditions, which in turn allows for the direction ofresources in a manner that allows such resources to efficient arrive ina given channel at a time when the resources are needed and/or otherwisedisposable through the channel.

To overcome these, and other technical challenges, exampleimplementations of embodiments of the invention described herein useautomated tools to acquire and scale diverse sets of information aboutthe channels (such as aggregators, for example) through which mobiledevices and/or other resources may be distributed. The scaledinformation can be used to assign groups of aggregators and/or otherchannels into tiers that generally reflect the ability of an aggregatorand/or other channel to effectively distribute the relevant resources.In order to effectively predict pricing information and otherwiseaddress time-sensitive and/or aged data, a decay function is modeled andotherwise applied to the pricing data received from the aggregatorsand/or other available channels (such as distribution channels wheremobile devices may be directly sold, for example). This combined tieringand data decay allow for an identification and ranking of aggregatorsand/or other channels that are likely to be able to distribute aparticular volume of specific devices at a predicted price at a time inthe future. As, such, resources can be directed to the appropriatechannels in time to take advantage of the optimum pricing and/ordistribution opportunities available at the time when the resources areavailable to be distributed. In situations where inventory is acquiredvia a secondary market (such as through buy-back programs, for example)the pricing and related conditions under which a particular deviceand/or set of devices can be calculated in view of the availabledistribution channels and forecasted sales price.

Many of the example implementations described herein are particularlyadvantageous in situations and other contexts that involve thedisposition of inventories of used mobile devices, such as theinventories acquired through insurance claims, buy-back programs,trade-in programs, and the like. In some such situations, theavailability of distribution channels, the viability of such channels,the existing inventory of devices, the value of those devices, and thedemand for such devise, all tend to vary with time. By predicting andmodeling the ability of one or more channels to receive and distributeone or more sets of mobile devices (and the terms, speed, and otheraspects of such receipt and distribution), resources (in the form ofused mobile devices, for example) can be efficiently distributed tocustomers and/or other potential users in a manner that closelytime-aligns device availability and demand. As such, and for purposes ofclarity, some of the example implementations described herein use terms,background facts, and details that are associated with deviceacquisition and distribution, and may reference information and dataobjects associated with the receipt and distribution of such used mobiledevices. However, it will be appreciated that embodiments of theinvention and example implementations thereof may be applicable andadvantageous in a broad range of contexts and situations outside ofthose related to event preparedness and planning.

Embodiments of the present disclosure are further directed tocomputer-implemented methods, apparatuses, systems, and computer programproducts for improved generation of resource offer sets, analysis and/oradjustment of generated resource offer sets, and/or approval of resourceoffer sets. More specifically, a predicted optimal resource offer setmay be modeled using a resource offer generation model. Variousdisparate and unstructured data sets (e.g., resource pricecharacteristics offered by third-party entities such as vendors andcompetitors, resource owner offered price characteristics, resourceinventory data, resource-related social media data, seasonality data,resource launch data, and the like) may be retrieved from one or moredisparate data sources, warehouses, datastores, and the like. Theunstructured data sets may be cleaned, normalized, transformed, andotherwise synthesized for applying to the resource offer generationmodel. By modeling optimal resource offers based on various datasources, example implementations of embodiments of the presentdisclosure are able to rapidly provide one or more resource offer sets(which may be time-sensitive or require careful tuning to be effectivein securing sufficient interest from resource owners) for purposes ofresource acquisition and subsequent distribution. Specifically, forexample in the environment of acquisition and distribution of usedmobile devices, a resource offer data object associated with purchase ofa used mobile device must be properly tuned so a corresponding pricecharacteristic or resource offer value is set such that device ownersare likely to take advantage of the offer (e.g., individual deviceowners may perform a trade-in via one or more device acquisitionchannels, such as a carrier), while ensuring that financial and/orbenchmarking targets (such as profitability, margin, desired deviceacquisition distribution, and the like) are satisfied with regard to theacquisition and expected distribution of the used mobile devicesassociated with the generated resource offer sets.

Acquisition and/or distribution of resources, including used mobiledevices, may change dynamically and significantly between regions and/orover time between regions or within a single region. For each region(e.g., country, city, or other defined geographic area) and collectionperiod (e.g., a time interval for which an offer defined by an resourceoffer data object may be actively provided for the region), a usedmobile device may be optimally associated with a particular resourceoffer data object in a generated resource offer set. For example, eachresource may be mapped to a particular resource offer data object, asdescribed herein, that represents a corresponding offer to be providedfor acquisition of the resource.

Resources may be identified based on their resource attributes and/or acorresponding resource set identifier, such as a CNN. For any givenresource associated with a corresponding CNN, an ideal resource offervalue for resource offer data objects associated with particularresource set identifier may vary with time and/or region, such that amobile device having certain attributes may be optimally associated witha first offer value at a first time and second offer value at a secondtime, or associated with a first offer value for a first region and asecond offer value for a second region. The offer value may also varydependent on various resource attributes associated with resource. Forexample, for a given mobile device, the functioning of the mobiledevice, in particular, may alter an ideal resource offer value for aresource offer data object associated with the resource. In an exampleenvironment, resources such as mobile devices that are only partiallyfunctioning may be associated with a lower offer value than a functionalmobile device. Between two resources with differing functionality, thedifference in resource offer value may be difficult to determine.

The inventors of the embodiments of the disclosure herein haverecognized that to provide an optimal resource offer data object for aparticular resource (e.g., associated with a particular resource setidentifier), an offer data object may be modeled and predicted based onvarious data sets comprising various types of data. Conventionalapproaches do not accurately consider resource distribution allocationchannels and expected distribution timeframes, promotional periods, andfair market offer values for a given resource, such as a used mobiledevice. Consequently, resource offer data objects may be generatedassociated with sub-optimal or inaccurately predicted offer values, andthus providing an offer defined by the resource offer data object ismore likely to be unsuccessful in obtaining the volume of desiredresources for distribution via various channels.

To address these and other technical challenges, users associated withrequests to generate resource offers (e.g., offer control users) mayinteract with a resource offer generation system that uses one or morepredictive, machine learning models. Through the use of the machinelearning models, the system is able to generate a resource offer setcomprising resource offer data objects for various resources associatedwith various resource set identifiers. The system may further optimizethe resource offer set to be provided based on desired benchmarkingand/or targets, such as financial and/or business parameters or goals,provided via a benchmark and portfolio target data set. The machinelearning models may be based on outputs by the prediction model toimprove generated resource offers meeting desired financial and/orbenchmarking targets. The machine learning models may utilize othermarket information data set(s) retrieved and synthesized for variousmobile devices having different attributes and characteristics, asdescribed above, and offered by various third-party entities (such ascompetitors, business-to-consumer entities, and the like). The resourceoffer generation system may similarly access the extracted, normalized,scaled, and/or otherwise conditioned information conventionallyunavailable due to data occlusion.

The inventors of embodiments of the present disclosure herein furtherrecognize that technical challenges are presented with providingresource data object sets for analyzing and, if desired, efficiently andeffectively adjusting resource offer data objects, for example to adjustcorresponding resource offer value(s) to meet new desired financial orbenchmark targets. A system user, for example an offer control user, maydesire to analyze the generated resource offer set to gauge the relativestrength of the resource offer set, visualizes the effects ofadjustments on the strength of the resource offer set and/or the effectsof adjustments on reaching benchmark and/or portfolio targets, forexample based on gathered and standardized market information todetermine whether the relative strength of the resource offer set (e.g.,chance that offers defined by each resource offer data object will beaccepted/utilized by a resource owner owners) of the generated resourceoffer set is sufficient and that the resource offer set will satisfydesired financial and benchmarking targets. Based on the analysis, thesystem user may desire to adjust one or more of the resource offer dataobjects in the resource offer set, such as to increase overall offerstrength or to improve benchmark or portfolio target metrics (e.g.,profitability).

In this regard, embodiments provide advantageous interfaces for viewing,analyzing, adjusting, and/or approving resource offer sets. Users mayaccess an offer adjustment interface via embodiments of the presentdisclosure. The offer adjustment interface may be configured to enable asystem user to view and analyze the resource offer set. The offeradjustment interface may further be configured to enable a system userto view and analyze additional information derived from or associatedwith the resource offer set. For example, the offer adjustment interfacemay include a dashboard for accessing various interfaces used inanalyzing the resource offer set. Additionally, the offer adjustmentinterface may include an indication of an offer analytics data setindicating financial metrics for the generated resource offer set, andupdated to reflect the current adjusted resource offer set asadjustments are made via the interface.

Further, a system user, such as an offer control user, may adjust theresource offers via the offer adjustment interface. Such adjustments maybe performed to meet new financial and/or benchmarking targets. As auser adjusts one or more resource offer data objects, the dashboardinterfaces and/or offer analytics data set associated with the resourceoffers is dynamically updated by the system to reflect calculationsbased on the adjusted resource offer set. Such embodiments providetechnical advantages in visualizing changes to prospective resourceoffers and effects on offer strength, and/or financial and/or benchmarktargets.

Submitted adjusted resource offer sets may be subject to approval byanother user, such as an offer approval user. Embodiment system mayfacilitate an improved approval process by providing an improved offerapproval interface. Via the offer approval interface, the offer approvaluser may effectively analyze the adjusted resource offer set submittedby the offer control user. The offer approval interface may include adashboard, such as the dashboard rendered associated with the offeradjustment interface, to enable efficient and thorough analysis usingspecific, streamlined interfaces.

Definitions

As used herein, the terms “data,” “content,” “information,” and similarterms may be used interchangeably to refer to data capable of beingtransmitted, received, and/or stored in accordance with embodiments ofthe present disclosure. Thus, use of any such terms should not be takento limit the spirit and scope of embodiments of the present disclosure.Further, where a computing device is described herein to receive datafrom another computing device, it will be appreciated that the data maybe received directly from another computing device or may be receivedindirectly via one or more intermediary computing devices, such as, forexample, one or more servers, relays, routers, network access points,base stations, hosts, and/or the like, sometimes referred to herein as a“network.” Similarly, where a computing device is described herein tosend data to another computing device, it will be appreciated that thedata may be sent directly to another computing device or may be sentindirectly via one or more intermediary computing devices, such as, forexample, one or more servers, relays, routers, network access points,base stations, hosts, and/or the like.

As used herein, the term “circuitry” refers to (a) hardware-only circuitimplementations (e.g., implementations in analog circuitry and/ordigital circuitry); (b) combinations of circuits and computer programproduct(s) comprising software and/or firmware instructions stored onone or more computer readable memories that work together to cause anapparatus to perform one or more functions described herein; and (c)circuits, such as, for example, a microprocessor(s) or a portion of amicroprocessor(s), that require software or firmware for operation evenif the software or firmware is not physically present. This definitionof “circuitry” applies to all uses of this term herein, including in anyclaims. As a further example, as used herein, the term “circuitry” alsoincludes an implementation comprising one or more processors and/orportion(s) thereof and accompanying software and/or firmware. As anotherexample, the term “circuitry” as used herein also includes, for example,a baseband integrated circuit or applications processor integratedcircuit for a mobile phone or a similar integrated circuit in a server,a cellular network device, other network device, and/or other computingdevice.

As used herein, a “computer-readable storage medium,” which refers to aphysical storage medium (e.g., volatile or non-volatile memory device),may be differentiated from a “computer-readable transmission medium,”which refers to an electromagnetic signal.

As used herein, the terms “user”, “client”, and/or “request source”refer to an individual or entity that is a source, and/or is associatedwith sources, of a request for an identification of one or more channelsfor use in the distribution of resources and/or related content to beprovided by a prediction control system and/or any other system capableof predicting and/or modeling the likely conditions of an environment inwhich the relevant resources may be distributed through one or moreknown channels. For example, a user and/or client may be the ownerand/or entity that seeks information regarding the optimum channel orchannels through which to distribute an inventory of certain used mobiledevices and/or the likely conditions under which the inventory ofcertain used mobile devices may be efficiently distributed.

The term “client device” refers to computer hardware and/or softwarethat is configured to access a service made available by a server. Theserver is often (but not always) on another computer system, in whichcase the client device accesses the service by way of a network. Clientdevices may include, without limitation, smart phones, tablet computers,laptop computers, wearables, personal computers, enterprise computers,and the like. Client devices, as described herein, communicate with andotherwise access a prediction system and/or resource offer generationsystem, via one or more networks.

The term “offer control user” refers to a particular user of a resourceoffer generation system permissioned to perform one or more actionsassociated with the resource offer generation system via a client devicecommunicable with the resource offer generation system. An offer controluser is associated with an offer control user account permissioned to,via a resource offer generation system, generate a resource offer dataset for a particular region-program identifier and collection perioddata object, view for analysis and adjust a resource offer data set fora particular region-program identifier and collection period data objectvia an offer adjustment interface, and/or submit a resource offer set,or adjusted resource offer set, for approval. An offer control user, insome embodiments, is associated with a corresponding user accountpermissioned to access the resource offer generation system forperforming the actions described. The offer control user mayauthenticate user credentials associated with the user account to beginan authenticated session and perform the actions described via theresource offer generation system.

The terms “color neutral name” or “CNN” refer to a system standardizedresource identifier that identifies resource associated with specificresource attributes. A CNN may be mapped to one or more third-partyresource identifiers, for example maintained by third-party databasesand/or devices. The term “resource attributes” refers to devicespecifications, characteristics, or identifying information associatedwith a particular resource. A resource may be categorized by itsresource attributes, such that resources having the same resourceattributes may be grouped and identified by a combination of theresource attributes. For example, in the context of distribution ofmobile devices as resources, a mobile device resource may be associatedwith a make identifier, model identifier, storage size identifier,and/or carrier identifier. In some embodiments, resource attributes mayinclude similar information associated with the specifications of theresource. A corresponding CNN may be associated with multiple country,region, or third-party specific identifiers used to characterizeresources of the same device.

The term “resource set identifier” refers to a unique string, number, orother form of identification that is associated with one or moreresources sharing at least one common attribute. In some embodiments, aresource set identifier is a CNN. In some embodiments, a resource setidentifier is a SKU. In other embodiments, a resource set identifier isone or more resource attribute or several resource attributes incombination.

The term “digital content item” refers to any electronic media contentitem that is intended to be used in either an electronic form or asprinted output and which may be received, processed, and/or otherwiseaccessible by a client device. A digital content item, for example, maybe in the form of a text file conveying human-readable information to auser of a client device. Other digital content items include images,audio files, video files, text files, and the like.

As used herein, the term “data object” refers to a structuredarrangement of data. A “request data object” is a data object thatincludes one or more sets of data associated with a request by a userfor an identification of one or more channels and/or the conditions ofone or more channels through which resources (such as mobile devices)may be distributed. A “channel context data object” is a data objectthat includes one or more sets of data that alone or in combination withother sets of data provide information about a channel and/orenvironment in which one or more channels may operate, such that aspectsof the one or more channels may be predicted.

As used herein, the term “data set” refers to a collection of data. Oneor more data sets may be combined, incorporated into, and/or otherwisestructured as a data object. A “context data set” is a data set thatincludes information regarding channel and/or environment in which oneor more channels may operate. A “predicted condition data set” is a dataset that contains one or more indications of a channel and/or relatedconditions through which resources (such as mobile devices, for example)may be distributed.

The term “third-party entity” refers to a company, individual, group, orthe like, that associated with resource acquisition and/or distribution.Examples of a third-party entity include, but are not limited to, acompetitor entity (an indirect or direct competitor entity) and adistributed user platform owner entity. Some third-party entities arecommercial acquirers and/or resellers of resources. In some embodiments,each third-party entity is associated with a particular channel profilefor distribution and/or acquisition of resources via the third-partyentity.

The term “region-program data object” refers to an electronicallymanaged structured arrangement of data associated with particularofferings associated with acquisition of resources for a particularregion. Each region-program data object may be associated with aparticular program for acquiring a set of resources based on anassociated approved resource offer set. Each region-program data objectmay be associated with a “region-program identifier” that uniquelyidentifies the region-program data object. A region may be associatedwith one or more region-program data objects.

The term “collection period data object” refers to an electronicallymanaged representation of a time interval defined by a collection periodstart timestamp and a collection period end timestamp. A resource offerset may be generated associated with a collection period data object,such that the resource offer set may be approved as valid associatedwith a region-program data object only during the time intervalrepresented by the collection period data object. For example, aparticular resource offer set may be associated with a particularprogram within a particular country for a two-week time intervalrepresented by a particular collection period data object.

The term “data collection parameter” refers to one or more parametersassociated with the acquisition of resources associated a particularregion-program data object. Data collection parameters include business,portfolio-level, and resource acquisition target parameters associatedwith the acquisition of resources associated with the region-programdata object. Non-limiting examples of data collection parameters includedistribution channel mix percentages, activity costs, resource volumemultipliers, promotional resource listings, commissions associated withresource offer data objects, offer ratios for functional andnon-functional resources, desired profit per device, volume percentagedesired by grade, time-based resource condition multipliers, and aminimum resource offer value for functional and/or non-functionalresources. A region-program data object may include, or be associatedwith, a “data collection parameter set” including one or more datacollection parameter(s) for that region-program data object.

The term “benchmark and portfolio target data set” refers to acollection of data representing or associated with target metrics forthe distribution and/or procurement of resources. In some embodiments,the benchmark and portfolio target data set represents a subset of thedata collection parameters. In some embodiments, a benchmark andportfolio target data set is associated with a region-program dataobject. In some embodiments, a benchmark and portfolio target data setdefines boundary conditions input by an offer control user or offerapproval user, such that a generated and/or submitted resource offer setmust satisfy the boundary conditions defined by the benchmark andportfolio target data set. For example, in some embodiments, thebenchmark and portfolio target data set includes at least a minimumexpected profitability based on the resource offer set or a minimumexpected margin based on the resource offer set. In some embodiments, abenchmark and portfolio target data set includes a target time intervalfor the distribution or acquisition of a number of resources.

The term “resource offer data object” refers to an electronicallymanaged structured arrangement of data that includes at least a resourceoffer value for a particular resource set identifier. The resource offerdata object may include a resource set identifier with which theresource offer value is associated. A resource offer data object isadjustable by a user, such as an offer control user, which alters theresource offer value associated with the resource offer data object.Each resource offer data object may be uniquely associated with aresource offer identifier.

The term “resource offer set” refers to a group of zero or more resourceoffer data objects. Each resource offer data object in a resource offerset may be associated with a different resource set identifier.

The term “adjustment data object” refers to an electronically managedstructured arrangement of data that represents a change in one or moreproperties associated with one or more resource offer data object(s). Insome embodiments, an adjustment data object includes an adjustedresource offer value for one or more resource offer data objects. One ormore adjustment data objects may be used to update a resource offer setto create an adjusted resource offer set.

The term “adjusted resource offer set” refers to a resource offer setincluding one or more adjustments to one or more resource offer dataobjects by an offer control user. In some embodiments, an adjustedresource offer set is created by updating a resource offer set based onone or more adjustment data objects. An adjusted resource offer set maybe further adjusted based on a second set of adjustment data objects tocreate a new adjusted resource offer set. In some embodiments, a storedresource offer set associated with a region-program identifier andcollection period data object is embodied by an adjusted resource offerset, for example after one or more adjustments are performed by an offercontrol user.

The term “offer status record” refers to electronically managed datastored in a repository associated with managing approval of a resourceoffer set associated with a region-program identifier and collectionparameter data object. In some embodiments, an offer status record isstored in an offer approval repository, which may be a sub-repositorymanaged by a resource offer generation system. An offer status record isretrievable associated with, based on, or utilizing the region-programidentifier and collection parameter data object. In some embodiments,the offer status record includes at least an offer status indicator. Insome embodiments, the offer status record is associated with, orotherwise linked to, the resource offer set.

The term “offer status indicator” refers to data or informationindicative of a process status for generation, adjustment, and approvalof a resource offer set associated with a particular region-program dataobject and collection period data object. In some embodiments, an offerstatus indicator is represented by one of a plurality of possible statusindicators. An example offer status indicator is a “requested statusindicator,” which indicates a resource offer generation process has beenhas been requested for a corresponding region-program identifier andcollection period data object, but the resource offer set is not yetgenerated. In some embodiments, another example offer status indicatoris a “pending adjustment status indicator,” which indicates a resourceoffer set has been generated for the region-program identifier andcollection period data object, but has not yet been submitted by anoffer control user for approval. In some embodiments, another exampleoffer status indicator is a “pending approval status indicator,” whichindicates an adjusted resource offer set has been submitted by an offercontrol user for approval or rejection by an offer approval user, buthas not yet been approved or rejected by an offer approval user. In someembodiments, another example offer status indicator is an “approvedstatus indicator,” which indicates a submitted adjusted resource offerset has been analyzed and/or approved by an offer approval user. In someembodiments, another example offer status indicator is a “rejectedstatus indicator,” which indicates a submitted adjusted resource offerset has been analyzed and/or rejected by an offer approval user.

In some embodiments, an offer status indicator is stored in, orassociated with, an offer status record corresponding to aregion-program identifier and collection period data object. The offerstatus record may be stored in an offer approval repository. In someembodiments, the offer status record similarly includes, or isassociated with, a stored resource offer set. In other embodiments, thestored resource offer set associated with the offer status record isstored in another repository or sub-repository.

The term “expected resource volume data set” refers to a collection ofdata associated with an expected channel-wise distribution of resourcesassociated with particular resource set identifiers. In someembodiments, an expected resource volume set data is output, or parsedfrom output, by a prediction system. In some embodiments, for example,an expected resource volume data set includes, or is derived from, atleast one resource allocation set generated by a prediction systemassociated with at least one channel profile. In some embodiments, anexpected resource volume data set is generated by another systemassociated with the prediction system.

The term “average distribution term data set” refers to a collection ofdata associated with parameters associated with the distribution ofresources identified by the expected resource volume data set. In someembodiments, the average distribution term data set includes at least anaverage selling price for which a resource is predicted to bedistributed. In some embodiments, an average distribution term data setis output, or parsed from output, by a prediction system.

The term “market intelligence data set” refers to a collection of datathat is associated with the acquisition and/or distribution of resourcesassociated with one or more channels by various entities. For example, amarket intelligence data set may include information regardingacquisition of resources associated with one or more channels, sentimentinformation associated with a resource, launch information associatedwith a resource, perceived value of a resource for distribution and/oracquisition. A market intelligence data set, or portions thereof, may beretrieved from one or more third-party systems, scraped from variousdata sources (e.g., web scraping), received from a third-party system(e.g., data updated at a regular interval), or the like. In someembodiments, a market intelligence data set includes one or moresubsets, each associated with a particular resource set identifier, suchas a CNN. In some embodiments, a market intelligence data set includes,for one or more particular resource set identifiers: distributed userplatform pricing for the particular resource set identifier (e.g.,average sales price for a particular resource set identifier via one ormore distributed user platforms, such as eBay™ or similar channels),other third-party offer values for the particular resource setidentifier, social media sentiment for the particular resource setidentifier, seasonality information, launch information associated withthe particular resource set identifier, and inventory data.

The term “exception period” refers to an untrusted timestamp intervalduring which a particular resource characteristic, for a particularresource in an untrusted third-party resource characteristic data set,is not within an expected operating range. In some embodiments, theexpected operating range is embodied by an expected deviation of anoffset between an untrusted third-party resource characteristic data setand a distributed resource characteristic data set. In some embodiments,an exception period begins at a first timestamp where a deviation in anoffset for the value of a particular resource characteristic satisfiesan exception deviation threshold, and ends at a second timestamp wherethe deviation in the offset for the value of the particular resourcecharacteristic does not satisfy the exception deviation threshold. Insome embodiments, an exception period for a particular untrustedthird-party resource characteristic data set includes one or morerecords of the untrusted third-party resource characteristic data setassociated with a timestamp that falls within the exception period.

The term “exception deviation threshold” refers to a normal operatingrange of a deviation of an offset between a resource characteristic ofan untrusted resource characteristic data set and the resourcecharacteristic of a distributed resource characteristic data set for aparticular resource set identifier. In some embodiments, an exceptionperiod is indicated when the deviation of the offset satisfies theexception deviation threshold by exceeding the exception deviationthreshold.

The term “exception detection model” refers to one or more machinelearning, algorithmic, and/or statistical models, or a combinationthereof, for generation of a trusted resource characteristic data setbased on one or more untrusted third-party resource characteristic dataset(s) applied to the model, and a distributed resource characteristicdata set applied to the model. In some embodiments, an exceptiondetection model is configured to identify an exception period set forthe applied untrusted third-party resource characteristic set based on adeviation in an offset with respect to a distributed resourcecharacteristic data set, remove the exception period set to create anupdated untrusted third-party resource characteristic data set, andgenerate the trusted resource characteristic data set based on at leastthe updated untrusted third-party resource characteristic data set. Insome embodiments, the exception detection model is configured togenerate the trusted resource characteristic data set based on acomparison between two or more updated untrusted third-party resourcecharacteristic data sets associated with different third-party entities.

The term “resource characteristic” refers to a particular attributeassociated with a resource. One or more resource characteristics for aresources associated with a particular resource set identifier arerepresented in a record of a data set associated with the resource setidentifier. For example, the terms “price characteristic” and “pricingcharacteristic” refer to an offer value for acquisition or distributionof resources associated with the corresponding resource set identifier.

The term “untrusted third-party resource characteristic data set” refersto a collection of one or more resource characteristics associated witha particular third-party entity, where the collection may include one ormore resource characteristics associated with an exception period.Untrusted third-party resource characteristic data sets described hereinare updated based on comparison to a distributed resource characteristicdata set. In some embodiments, the untrusted third-party resourcecharacteristic data set includes at least a price characteristic for aparticular resource, such as a used mobile device.

The term “third-party resource pricing data set” refers to a particular,historical data set representing an untrusted third-party resourcecharacteristic data set including at least a pricing characteristic forone or more resources of resource set identifiers. In some embodiments,the third-party resource pricing data set is associated with athird-party entity providing a third-party offer reflected as a recordof the third-party resource pricing data set. In some embodiments, athird-party resource pricing data set is included in a marketintelligence data set.

The term “distributed user platform” refers to a marketplace or otherplatform configured to enable individual users to generate offers forthe purposes of resource acquisition and/or distribution. In someembodiments, a distributed user platform comprises one or moredistributed third-party entity devices configured to enable access tothe distributed user platform. In some embodiments, the offers includeat least a price characteristic for a particular resource setidentifier. The distributed user platform is associated with acorresponding third-party entity in control of the distributed userplatform.

The term “distributed resource pricing data set” refers to a particularhistorical data set including at least a price characteristic for one ormore resources or resource set identifiers. In some embodiments, thedistributed resource pricing data set is associated with user-generatedoffers for resource acquisition available for one or more resources orresource set identifiers via a distributed user platform.

The term “alignment” refers to an organization and/or sorting of one ormore data sets based on one or more characteristics of each record. Theterm “temporal alignment” refers to a particular organization of one ormore data sets based on an associated timestamp characteristic. The term“resource set identifier alignment” refers to a particular organizationof one or more data sets based on an associated resource set identifier.

The term “resource offer generation request” refers to a transmission bya client device associated with an offer control user to a resourceoffer generation system indicating a request to generate a resourceoffer set associated with a region-program identifier and collectionperiod data object. In some embodiments, an offer request comprises atleast the region-program identifier and collection period data objectfor which the resource offer set is to be generated. The resource offerset may be generated associated with various resource set identifiersdetermined based on the region-program data object associated with theregion-program identifier.

The term “indication” refers to a data or information representing avisual presentation of data, a data object, a set of data, or a portionof any thereof, to a particular user interface. Examples of indicationsinclude, but are not limited to, a text indication, a graphicalindication, a chart indication, a pictorial indication, and an encodedindication. It should be appreciated that an indication may causedisplaying and/or rendering of the visual presentation of the data, dataobject, set of data, or a portion of any thereof, to the user interface.

Example System Environment

Turning now to the Figures, FIG. 1 shows an example system environment100 in which implementations involving the efficient prediction andmodeling of conditions and channels through which resources may bedistributed may be realized. The depiction of environment 100 is notintended to limit or otherwise confine the embodiments described andcontemplated herein to any particular configuration of elements orsystems, nor is it intended to exclude any alternative configurations orsystems for the set of configurations and systems that can be used inconnection with embodiments of the present disclosure. Rather, FIG. 1and the environment 100 disclosed therein is merely presented to providean example basis and context for the facilitation of some of thefeatures, aspects, and uses of the methods, apparatuses, and computerprogram products disclosed and contemplated herein. It will beunderstood that while many of the aspects and components presented inFIG. 1 are shown as discrete, separate elements, other configurationsmay be used in connection with the methods, apparatuses, and computerprograms described herein, including configurations that combine, omit,and/or add aspects and/or components.

Embodiments implemented in a system environment such as systemenvironment 100 advantageously provide for the efficient prediction andmodeling of conditions and channels through which resources may bedistributed by receiving and parsing a request data object received froma user, retrieving and/or receiving a set of data objects and/or otherdata sets to be presented to a machine learning model (such as one ormore channel context data objects, for example), retrieving a predictedcondition data set by applying the received data objects to a machinelearning model, and generating a control signal causing a renderableobject associated with the predicted condition data set to be displayedon a user interface of a client device associated with the user. Somesuch implementations contemplate the use of channel context data objectsand/or other data sets associated with distribution channels and/or themobile device or other resource that is the subject of a given requestdata object. Some such embodiments leverage a hardware and softwarearrangement or environment for the efficient prediction and modeling ofconditions and channels through which resources may be distributed andresponsive message generation actions described, contemplated, and/orotherwise disclosed herein.

As shown in FIG. 1, a prediction system 102 includes an onlineprediction system module 102A which is configured to receive, process,transform, transmit, communicate with and evaluate request data objects,channel context data objects, the content and other informationassociated with such data objects, other data sets, and relatedinterfaces via a web server, such as prediction system server 102Band/or prediction system device 102D. The prediction system server 102Band/or prediction system device 102D is connected to any of a number ofpublic and/or private networks, including but not limited to theInternet, the public telephone network, and/or networks associated withparticular communication systems or protocols, and may include at leastone memory for storing at least application and communication programs.

It will be appreciated that all of the components shown FIG. 1 may beconfigured to communicate over any wired or wireless communicationnetwork including a wired or wireless local area network (LAN), personalarea network (PAN), metropolitan area network (MAN), wide area network(WAN), or the like, as well as interface with any attendant hardware,software and/or firmware required to implement said networks (such asnetwork routers and network switches, for example). For example,networks such as a cellular telephone, an 802.11, 802.16, 802.20 and/orWiMax network, as well as a public network, such as the Internet, aprivate network, such as an intranet, or combinations thereof, and anynetworking protocols now available or later developed including, but notlimited to TCP/IP based networking protocols may be used in connectionwith system environment 100 and embodiments of the invention that may beimplemented therein or participate therein.

As shown in FIG. 1, prediction system 102 also includes a predictiondatabase 102C that may be used to store information associated withrequest data objects, users, resources (such as used mobile devices, forexample) and/or channels associated with request data objects, channelcontext data objects, other data sets, interfaces associated with anysuch data objects or data sets, request source systems, channel contentsystems, and/or any other information related to the efficientprediction and modeling of conditions and channels through whichresources may be distributed and the generation of one or more relatedmessages and/or digital content item sets. The prediction database 102Cmay be accessed by the prediction system module 102A, the predictionsystem server 102B, and/or the prediction system device 102D, and may beused to store any additional information accessed by and/or otherwiseassociated with the prediction system 102 and/or its component parts.While FIG. 1 depicts prediction system database 102C as a singlestructure, it will be appreciated that prediction system database 102Cmay additionally or alternatively be implemented to allow for storage ina distributed fashion and/or at facilities that are physically remotefrom the each other and/or the other components of prediction system102.

Prediction system 102 is also shown as including prediction systemdevice 102D which may take the form of a laptop computer, desktopcomputer, or mobile device, for example, to provide an additional means(other than via a user interface of the prediction system server 102B)to interface with the other components of prediction system 102 and/orother components shown in or otherwise contemplated by systemenvironment 100.

Request data objects, request data object information and/or additionalcontent or other information to be associated with one or more requestdata objects may originate from a request source system such as requestsource system 104. A user of request source system 104 may use a requestsource device 104B, such as a laptop computer, desktop computer, ormobile device, for example, to interface with a request source module104A to create, generate, and/or convey a request data object and/orinformation to be included in a request data object, such as anidentification of one or more resources (such as mobile deviceidentification information, inventory information, timing information,and/or other request parameters, for example). The request source system104 may (such as through the operation of the request source module 104Aand/or the request source device 104B, for example) transmit a requestdata object to the prediction system 102. While only one request sourcesystem 104 is depicted in FIG. 1 in the interest of clarity, it will beappreciated that numerous other such systems may be present in systemenvironment 100, permitting numerous users and/or other request sourcesto develop and transmit request data object and/or informationassociated with request data objects to prediction system 102.

As shown in FIG. 1, system environment 100 also includes content system106, which comprises a content module 106A, a content server 106B, and acontent system database 106C. While only one content system 106 isdepicted in FIG. 1 in the interest of clarity, it will be appreciatedthat numerous additional such systems may be present in systemenvironment 100, permitting numerous sources of channel context contentand/or other information relevant to the efficient prediction andmodeling of conditions and channels through which resources may bedistributed to communicate and/or otherwise interact with the predictionsystem 102 and/or one or more request source systems 104. As shown inFIG. 1, the content system 106 is capable of communicating withprediction system 102 to provide information that the prediction system102 may need when predicting and modeling conditions and channelsthrough which resources may be distributed. For example, content system106 may, such as via the capabilities and/or actions of the contentmodule 106A, content system server 106B, and/or content system 106C,obtain and provide information associated with one or more mobiledevices, distribution channels, mobile device data, dispositioninformation, market condition information, macroeconomic data, and/orother device- or channel-related data, for example.

Content system 106 is also shown as optionally being capable ofcommunicating with request source system 104. In some situations, suchas when a given content system 106 is associated with content owned byand/or otherwise controlled by a user of a request source system, it maybe advantageous for the content system 106 to interface with and/orotherwise be in communication with the request source system 104 ingeneral and the request source device 104B in particular to captureand/or otherwise process such content.

Overall, and as depicted in system environment 100, prediction system102 engages in machine-to-machine communication with request sourcesystem 104 and context content system 106, via one or more networks, tofacilitate the processing of request data objects received from a user,the efficient prediction and modeling of conditions and channels throughwhich resources may be distributed, the retrieval and/or generation of adigital content item set and/or other data set based at least in part onthe request data object at, and the generation and/or transmission of acontrol signal causing a renderable object associated with the predictedchannel and/or condition to be displayed on a user interface of a clientdevice associated with the user.

Example Apparatus for Implementing Improved Channel Prediction andModeling

It will be appreciated that the prediction system 102 may be embodied byone or more computing systems, such as apparatus 200 shown in FIG. 2. Asillustrated in FIG. 2, the apparatus 200 may include a processor 202, amemory 204, input/output circuitry 206, communications circuitry 208,prediction circuitry 210, and content aggregation circuitry 212. Theapparatus 200 may be configured to execute any of the operationsdescribed herein.

Regardless of the manner in which the apparatus 200 is embodied, theapparatus of an example embodiment is configured to include or otherwisebe in communication with a processor 202 and a memory device 204 andoptionally the input/output circuitry 206 and/or a communicationscircuitry 208. In some embodiments, the processor (and/or co-processorsor any other processing circuitry assisting or otherwise associated withthe processor) may be in communication with the memory device via a busfor passing information among components of the apparatus. The memorydevice may be non-transitory and may include, for example, one or morevolatile and/or non-volatile memories. In other words, for example, thememory device may be an electronic storage device (e.g., a computerreadable storage medium) comprising gates configured to store data(e.g., bits) that may be retrievable by a machine (e.g., a computingdevice like the processor). The memory device may be configured to storeinformation, data, content, applications, instructions, or the like forenabling the apparatus to carry out various functions in accordance withan example embodiment of the present disclosure. For example, the memorydevice could be configured to buffer input data for processing by theprocessor. Additionally or alternatively, the memory device could beconfigured to store instructions for execution by the processor.

As described above, the apparatus 200 may be embodied by a computingdevice. However, in some embodiments, the apparatus may be embodied as achip or chip set. In other words, the apparatus may comprise one or morephysical packages (e.g., chips) including materials, components and/orwires on a structural assembly (e.g., a baseboard). The structuralassembly may provide physical strength, conservation of size, and/orlimitation of electrical interaction for component circuitry includedthereon. The apparatus may therefore, in some cases, be configured toimplement an embodiment of the present disclosure on a single chip or asa single “system on a chip.” As such, in some cases, a chip or chipsetmay constitute means for performing one or more operations for providingthe functionalities described herein.

The processor 202 may be embodied in a number of different ways. Forexample, the processor may be embodied as one or more of varioushardware processing means such as a coprocessor, a microprocessor, acontroller, a digital signal processor (DSP), a processing element withor without an accompanying DSP, or various other processing circuitryincluding integrated circuits such as, for example, an ASIC (applicationspecific integrated circuit), an FPGA (field programmable gate array), amicrocontroller unit (MCU), a hardware accelerator, a special-purposecomputer chip, or the like. As such, in some embodiments, the processormay include one or more processing cores configured to performindependently. A multi-core processor may enable multiprocessing withina single physical package. Additionally or alternatively, the processormay include one or more processors configured in tandem via the bus toenable independent execution of instructions, pipelining and/ormultithreading.

In an example embodiment, the processor 202 may be configured to executeinstructions stored in the memory device 204 or otherwise accessible tothe processor. Alternatively or additionally, the processor may beconfigured to execute hard coded functionality. As such, whetherconfigured by hardware or software methods, or by a combination thereof,the processor may represent an entity (e.g., physically embodied incircuitry) capable of performing operations according to an embodimentof the present disclosure while configured accordingly. Thus, forexample, when the processor is embodied as an ASIC, FPGA or the like,the processor may be specifically configured hardware for conducting theoperations described herein. Alternatively, as another example, when theprocessor is embodied as an executor of software instructions, theinstructions may specifically configure the processor to perform thealgorithms and/or operations described herein when the instructions areexecuted. However, in some cases, the processor may be a processor of aspecific device (e.g., a pass-through display or a mobile terminal)configured to employ an embodiment of the present disclosure by furtherconfiguration of the processor by instructions for performing thealgorithms and/or operations described herein. The processor mayinclude, among other things, a clock, an arithmetic logic unit (ALU) andlogic gates configured to support operation of the processor.

In some embodiments, the apparatus 200 may optionally includeinput/output circuitry 206, such as a user interface that may, in turn,be in communication with the processor 202 to provide output to the userand, in some embodiments, to receive an indication of a user input. Assuch, the user interface may include a display and, in some embodiments,may also include a keyboard, a mouse, a joystick, a touch screen, touchareas, soft keys, a microphone, a speaker, or other input/outputmechanisms. Alternatively or additionally, the processor may compriseuser interface circuitry configured to control at least some functionsof one or more user interface elements such as a display and, in someembodiments, a speaker, ringer, microphone and/or the like. Theprocessor and/or user interface circuitry comprising the processor maybe configured to control one or more functions of one or more userinterface elements through computer program instructions (e.g., softwareand/or firmware) stored on a memory accessible to the processor (e.g.,memory device 204, and/or the like).

The apparatus 200 may optionally also include the communicationcircuitry 208. The communication circuitry 208 may be any means such asa device or circuitry embodied in either hardware or a combination ofhardware and software that is configured to receive and/or transmit datafrom/to a network and/or any other device or module in communicationwith the apparatus. In this regard, the communication interface mayinclude, for example, an antenna (or multiple antennas) and supportinghardware and/or software for enabling communications with a wirelesscommunication network. Additionally or alternatively, the communicationinterface may include the circuitry for interacting with the antenna(s)to cause transmission of signals via the antenna(s) or to handle receiptof signals received via the antenna(s). In some environments, thecommunication interface may alternatively or also support wiredcommunication. As such, for example, the communication interface mayinclude a communication modem and/or other hardware/software forsupporting communication via cable, digital subscriber line (DSL),universal serial bus (USB) or other mechanisms.

As shown in FIG. 2, the apparatus may also include prediction circuitry210. The prediction circuitry 210 includes hardware configured tomaintain, manage, and provide access to a predictive model and/orinformation used by the predictive model to predict and model conditionsand channels through which resources may be distributed. The predictioncircuitry 210 may provide an interface, such as an applicationprogramming interface (API), which allows other components of a systemto obtain information associated with one or more resources and/orchannels and/or information associated with the channels through whichone or more sets of resources (such as mobile devices, for example) maybe efficiently distributed. For example, the prediction circuitry 210may facilitate access to and/or processing of information regardingcertain inventory, its features, the relevant market environment, and/orother information that may be used to predict and model conditions andchannels through which resources may be distributed, including but notlimited to any of the information that may be obtainable from and/orotherwise associated with a content system 106.

The prediction circuitry 210 may facilitate access to the channelcontext information and/or other information used by the predictivemodel through the use of applications or APIs executed using aprocessor, such as the processor 202. However, it should also beappreciated that, in some embodiments, the prediction circuitry 210 mayinclude a separate processor, specially configured field programmablegate array (FPGA), or application specific interface circuit (ASIC) tomanage the access and use of the relevant data. The prediction circuitry210 may also provide interfaces allowing other components of the systemto add or delete records to the prediction system database 102C, and mayalso provide for communication with other components of the systemand/or external systems via a network interface provided by thecommunications circuitry 208. The prediction circuitry 210 may thereforebe implemented using hardware components of the apparatus configured byeither hardware or software for implementing these planned functions.

The content aggregation circuitry 212 includes hardware configured tomanage, store, process, cleanse, scale, normalize and analyze a channelcontext data object, as well as the data sets and other information thatmay contained in and/or used to generate a channel context data object.Because the information that may be accessed and used to create channelcontext data objects may change frequently and/or be subject to controlby other systems, it may be desirable to maintain a content aggregationdatabase separate from prediction database 102C and/or the memory 204described above. It should also be appreciated though, that in someembodiments the prediction circuitry 210 and the content aggregationcircuitry 212 may have similar and/or overlapping functionality. Forexample, both the prediction circuitry 210 and the content aggregationcircuitry 212 may interact with one or more data objects associated withthe context within which a channel resides. The content aggregationcircuitry 212 may also provide access to other historical information,such as prior information sets presented to users with respect to agiven set of mobile devices (or other resources) and the channel orchannels used to efficiently distribute such devices or other resources.

Example Functional Implementation of Embodiments of the PresentDisclosure

FIG. 3 is a block a block diagram depicting a functional overview of asystem 300 in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3, the system 300 incorporates threeprimary functional blocks, including a user interface block 302, a datawarehouse block 304, and a supporting systems block 306, which arearranged such that each functional block is capable of communicatingwith the other functional blocks within system 300.

As shown in FIG. 3, the user interface block 300 includes one or moreinterface modules 302A-302N. In some example implementations, the system300 is designed to interact with a range of internal and/or external (orthird party, for example) users. In the context of a system designed topredictively identify one or more channels through which used mobiledevices should be efficiently distributed, the system 300 may be used byone or more internal users (such as users associated with entitiesresponsible for distributing the used mobile devices into theappropriate channel(s) and/or one or more external entities, such asaggregators responsible for collecting inventory for redistribution bythe system 300. In such example implementations, one interface module,such as interface module 302A, may provide for the functions, accesscontrols and/or other aspects of a user interface necessary for aninternal user to operate and/or otherwise use the system 300 topredictively identify the appropriate channels through which to directthe mobile device and the conditions (such as capacity, pricing, and/orother factors) that apply to the one or more identified channels.Likewise, the user interface may use another module, such as interfacemodule 302N, for example, to provide for the functions, access controls,and/or other aspects of a user interface necessary for an external user(such as an aggregator, for example) to interact with the system 300.

Similar to the user interface 302, the data warehouse block 304 and thesupporting systems block 306 each incorporate one or more functionalmodules, shown as warehouse modules 304A-304N and support modules306A-306N. In some example implementations, one of the modules providesfunctionality associated with resource demand planning and forecasting,which may involve the optimization of one or disposition channels basedon information regarding available supplies of resources, demand forsuch resources, strategic parameters and/or other business rules, andother information associated with inventory and/or inventory visibility.In some such example implementations one or more modules associated withthe data warehouse block 304 and/or the supporting systems block 306 maygenerate an expected device list containing information regarding thelikely inventory of mobile devices to be held by the system 300 andinformation associated with the channels into which such inventory maybe disposed.

In some example implementations, one of the modules providesfunctionality associated with aggregator management, which may include,but is not limited to, the management of aggregator-relatedapplications, account profiles, purchase histories, tiered and/or otherrankings, bidding and negotiation functions, purchase orders, trackingof financial transactions, and/or invoicing. In some such exampleimplementations, one or more modules associated with the data warehouseblock 304 and/or supporting systems block 306 may allow for on-boardingof potential aggregators, the ranking of aggregators, the receipt andprocessing of bids received from aggregators, the receipt and processingof purchase orders, and/or invoicing functions.

In some example implementations, one of the modules providesfunctionality associated with resource and/or other asset recovery anddisposition, which may include, but is not limited to, the management ofdata sets and/or other information necessary to detect and documentinventory, and manage the pricing and/or other aspects of inventoryallocation. In some such example implementations, one or more modulesassociated with the data warehouse block 304 and/or supporting systemsblock 306 may facilitate the generation of periodic inventory updates,the initiation of pricing and allocation assignments for use withaggregators, the analysis of aggregator bids, and/or the analysis andapproval of pricing and/or other offer conditions associated withaggregators.

In some example implementations, one of the modules providesfunctionality associated with materials management, which may include,but is not limited to, the management of inventory sorting operations,repair of bulk materials, aggregator skid reports, and/or materialsshipping. In some such example implementations, one or more modulesassociated with the data warehouse block 304 and/or the supportingsystems block 306 may facilitate the development, receipt, and/ortransmission of inventory sorting instructions (such as instructionsassociated with inventory liquidation, for example), the uploadingand/or other processing of aggregator skid reports, and/or processesassociated with the shipping of resources (such as mobile devices and/orother merchandise, for example).

In some example implementations, one of the modules providesfunctionality associated with accounting and/or finance operations,which may include, but are not limited to, management of thedetermination of costs, pricing, and/or other conditions associated withthe generation of invoices. In some such example implementations, one ormore modules associated with the data warehouse block 304 and/or thesupporting systems block 306 may facilitate the generation of entriesfor use in aggregator material allocation, logging of invoices, and/orother accounting operations.

In some example implementations, one of the modules providesfunctionality associate with enterprise sourcing operations, which mayinclude, but are not limited to, the administration of the relationshipsbetween the system operator and the related aggregators. In some suchexample implementations, one or more modules associated with the datawarehouse block 304 and/or the supporting systems block 306 facilitatethe creation and management of documentation to be used in connectionwith the relationship between an entity operating the system and one ormore aggregators or other third-party users.

Example Data Flow Diagram of Embodiments of the Present Disclosure

FIG. 4 is a block diagram depicting an example data flow through asystem 400 that may be used in connection with example implementationsof embodiments of the invention. As shown in FIG. 4, the system 400includes a portal user interface services module 402 that is configuredto send and receive information (such as request data objects associatedwith requests for identifications and/or allocations of channels throughwhich mobile devices may be efficiently distributed) from an internaluser 404A and/or an external user 404B. The portal user interfaceservices module 402 is also configured to send and receive informationfrom one or more data repositories 410A-410N, some of which may beconfigured to interact with a disposition database 406 and/or aninventory system 408.

In some example implementations, a user, such as internal user 404Aand/or external user 404B transmits a request data object and/or otherinformation associated with a request for an identification of one ormore channels through which resources (such as mobile devices) may bedisposed of, and the pricing and/or other conditions associated withdirecting the resources through the channel or channels. Upon receivingsuch a request, the portal user interface services module 402 mayinteract with one or more of the data repositories 410A-410N to send andreceive information to be used in connection with fulfilling theparameters of the request data object.

For example, the portal user interface services module 402 may interactwith a data warehouse, such as data repository 410A, which containsinformation associated with resource demand planning and forecasting. Insome such example implementations, the portal user interface servicesmodule 402 and the relevant data repository may create, exchange, and/ormodify material lists and details associated with the relevantresources. In some such example implementations, the data repository410A may also interact with the disposition database 406 to acquire alist and/or related information regarding the expected resourceinventory (such as an identification of the mobile devices expected tobe in inventory at a given time, for example).

In some example implementations, the portal user interface servicesmodule 402 may interact with a data repository, such as data repository410B, which contains information associated with asset distribution. Insome such example implementations, the portal user interface servicesmodule 402 and the relevant data repository may create, exchange, and/ormodify inventory lists and/or sale information associated with therelevant resources to be distributed.

In some example implementations, the portal user interface servicesmodule 402 may interact with a data repository, such as data repository410C, which contains information associated with buyback pricing and/orother buyback parameters. In some situations arising in contextsinvolving used mobile devices, one source of mobile device inventory mayinclude buyback systems and/or other arrangements where an entity buys amobile device from a user pursuant to the conditions of an insurancecoverage agreement, a buyback program, and/or other approach toacquiring used devices. In some such example implementations, the portaluser interface services module 402 and the relevant data repository maycreate, exchange, and/or modify bids and/or other negotiationinformation to facilitate the acquisition of inventory.

In some example implementations, the portal user interface servicesmodule 402 may interact with a data repository, such as data repository410D, for example, which contains information associated with materialmanagement functions. In some such example implementations, the portaluser interface services module 402 and the relevant data repository maycreate, exchange, and/or modify information associated with the price,cost, and/or other conditions imposed on a given set of materials and/orother resources, including but not limited to invoices. In some suchexample implementations, the relevant data repository may also interactwith inventory system 408 to exchange information associated with thegrading and/or sorting of material to be allocated, lot skid reports,and/or lot shipping releases.

In some example implementations, the portal user interface servicesmodule 402 may interact with a data repository, such as data repository410N, for example, which contains information associated with aggregatormanagement functions. In some such example implementations, the portaluser interface services module 402 and the relevant data repository maycreate, exchange, and/or modify information associated with aggregatorapplication submissions, the management of aggregator profiles and/oraccounts, and the submission of purchase orders.

Overall, as shown in FIG. 4, the system 400 is capable of leveraging awide variety of data sets and data sources to acquire and process theinformation necessary to identify one or more channels through whichresources may be efficiently distributed at a given time, and manage thefunctions necessary to ensure the efficient movement of resourceinventory in accordance with the predicted and modeled channels.

Example Processes for Channel Prediction and Modeling

FIG. 5 is a block diagram showing an example data flow 500 that may beused in connection with the efficient prediction and modeling ofconditions and channels through which resources may be distributed. InFIG. 5, a predictive modeler 508 is configured to receive a request dataobject from a user, such as via the interfaces shown and discussed inconnection with FIGS. 2, 3, and 4. In some example implementations, uponreceipt of a request data object, the predictive modeler 508 may, suchas in connection with a master data aggregation manager 504, leveragedata sets from a wide range of sources, shown as data repositories,502A-502N.

One such repository from which the predictive modeler 508 and/or themaster data aggregation manager 504 may receive channel context dataand/or other information relevant to the efficient prediction andmodeling of the distribution of resources, such as mobile devices, forexample, through one or more channels is an asset disposition datarepository, which may include, for example, information regarding howone or more sets of particular resources have been efficientlydistributed in the past. Such a repository may include (or otherwisehave access to) information scraped, extracted, and/or otherwiseacquired from one or more records of past resource allocations and/orinformation regarding the outcomes of such allocations.

Another repository from which the predictive modeler 508 and/or themaster data aggregation manager 504 may receive channel context dataand/or other information relevant to the efficient prediction andmodeling of the distribution of resources through one or more channelsis data repository which may include, for example, information regardingseasonal changes and/or other time-related factors impact the demand,availability, utility, and/or perceived value of one or more sets ofresources. Such a repository may include (or otherwise have access to)information scraped, extracted, and/or otherwise acquired from one ormore records of past resource allocations and/or information regardingthe outcomes of such allocations, and/or studies into such seasonaland/or other time-based effects.

Another repository from which the predictive modeler 508 and/or themaster data aggregation manager 504 may receive channel context dataand/or other information relevant to the efficient prediction andmodeling of the distribution of resources through one or more channelsis data repository which may include, for example, information regardingpast sales and/or other distributions of resources. Such a repositorymay include (or otherwise have access to) information scraped,extracted, and/or otherwise acquired from one or more records of pastbusiness-to-business and/or business-to-customer sales.

Another repository from which the predictive modeler 508 and/or themaster data aggregation manager 504 may receive channel context dataand/or other information relevant to the efficient prediction andmodeling of the distribution of resources through one or more channelsis data repository which may include, for example, information regardingresource attributes. Such a repository may include (or otherwise haveaccess to) information regarding the structure, function, operation,use, age, features, and/or other characteristics of the used mobiledevices in inventory, and/or may also include information relevant to adetermination of whether, and to what degree, the mobile devices canmeet the functional expectations of one or more sets of potentialcustomers.

Another repository from which the predictive modeler 508 and/or themaster data aggregation manager 504 may receive channel context dataand/or other information relevant to the efficient prediction andmodeling of the distribution of resources through one or more channelsis data repository which may include, for example, information regardingthe market and/or other environment within which certain relevantchannels may reside. Such a repository may include (or otherwise haveaccess to) information scraped, extracted, and/or otherwise acquiredfrom one or more records of activities conducted by competitors and/orother actors in the market and/or analyses of such activities.

Another repository from which the predictive modeler 508 and/or themaster data aggregation manager 504 may receive channel context dataand/or other information relevant to the efficient prediction andmodeling of the distribution of resources through one or more channelsis data repository which may include, for example, information regardingclaims made in connection with one or more resources, such as mobiledevices, for example. In contexts where all or a portion of theinventory of mobile devices to be distributed is acquired in connectionwith insurance coverage agreements and/or related programs, informationregarding the claims made on an individualized and/or aggregated basedmay be useful in capturing the change in the utility and value of amobile device as it ages. Such a repository may include (or otherwisehave access to) information scraped, extracted, and/or otherwiseacquired from one or more records of claims made with respect to one ormore devices. This information may include, for example, troubleshootingdata, device data, customer service data, and/or repair data used inconnection with determining whether the device is eligible for certaininsurance coverage and/or buyback.

Another repository from which the predictive modeler 508 and/or themaster data aggregation manager 504 may receive channel context dataand/or other information relevant to the efficient prediction andmodeling of the distribution of resources through one or more channelsis data repository which may include, for example, information regardingsocial media information and/or macroeconomic indicators. Such arepository may include (or otherwise have access to) informationscraped, extracted, and/or otherwise acquired from social media sites,economic analyses, and/or other sources of information designed tocapture individualized and/or aggregated views of the overall economy,particular devices, and/or other factors that may influence theperception of value held by one or more potential customers.

As shown in FIG. 5, the predictive modeler 508 and/or the master dataaggregation manager 504 are capable of interacting with a broad range ofdata sets from a wide array of sources. In some example implementations,such as when the data sets are acquired in multiple different formats,for example, the master data aggregation manager 504 may work inconjunction with a data filtering manager 506 to scale, cleanse,normalize, and/or otherwise format the various data sets such that theycan be processed by the predictive modeler 508. Upon receipt ofaggregated data from the master data aggregation manager 504, thepredictive modeler applies a predictive model to develop one or moremodel outputs, shown in FIG. 5 as model outputs 510A-510N. For example,in situations where a given inventory incorporates mobile devices frommultiple different manufacturers, each of the model outputs 510A-510Nmay provide an identification of a particular channel in either or bothof a business-to-business and/or business-to-customer contexts throughwhich a given portion of the inventory may be distributed, and anindication of the pricing and/or other conditions that may apply.

In some example implementations of data flow 500, the prediction modeler508 may employ a MARS model and/or another machine learning or atrainable model such that, over time, the prediction modeler 508,through receiving a plurality of user confirmations, may improve thedetermination of a one or more channels and/or conditions through whichresources may be efficiently distributed.

In some such embodiments, the prediction modeler 508 may employ machinelearning, or equivalent technology to improve the prediction andmodeling of channels and conditions through which resources may beefficiently distributed. In some examples, the prediction modeler 508may generally provide a trained model that is given a set of inputfeatures, and is configured to provide an output of a score (such as areliability score, for example), a recommendation, or the like. In someembodiments, a trained model can be generated using supervised learningor unsupervised learning. In some examples, such learning can occuroffline, in a system startup phase, or could occur in real-time or nearreal-time during performing the methods shown in the described figures(e.g., predicting and modeling an optimum channel for the distributionof resources). The trained model may comprise the results of clusteringalgorithms, classifiers, neural networks, ensemble of trees in that thetrained model is configured or otherwise trained to map an input valueor input features to one of a set of predefined output scores orrecommendations, and modify or adapt the mapping in response tohistorical data returned from previous iterations (e.g., measureddistributions, such as those derived from available data).

Alternatively or additionally, the trained model may be trained toextract one or more features from historical data using patternrecognition, based on unsupervised learning, supervised learning,semi-supervised learning, reinforcement learning, association ruleslearning, Bayesian learning, solving for probabilistic graphical models,among other computational intelligence algorithms that may use aninteractive process to extract patterns from data. In some examples, thehistorical data may comprise data that has been generated using userinput, crowd based input or the like (e.g., user confirmations).

In some examples, the prediction modeler 508 may be configured to applya trained model to one or more inputs to identify a set of reliabilityscores. For example, if the input feature was competitive salesinformation, such as may be obtained from one or more data sources, theprediction modeler 508 may apply such data to the trained model todetermine whether the resulting predicted channel and/or pricing isaccurate. In some examples, the trained model would output a suggestedreliability score based on other predictions and/or measurements usingthe same data.

Regardless of the precise configuration of the prediction modeler 508,upon receipt of a request data object (and any necessary extraction orparsing of data and/or other request-related data contained therein) theprediction modeler 508 retrieves and/or otherwise receives one or moredata objects from the repositories 502A-502N and determines the channel,pricing, and/or other conditions that apply to the predicted dispositionof the inventory referenced in the request data object.

FIG. 6 is a flow chart of an example process 600 for predicting andmodeling one or more channels and/or conditions that allow for theefficient distribution of resources in a given environment. As shown atblock 602, process 600 begins with receiving a request data object froma user. The request data object may incorporate a wide range ofinformation and be expressed in any format that allows for thetransmission of a request data object from a system associated with auser, such a request source system 104, for example, to a machinelearning model and/or a system associated with such a model. In general,a request data object will incorporate information sufficient toidentify the inventory and/or other resources associated with therequest, and may further identify a time and/or other conditions thatimpact the likely disposition of the inventory at a future time. In someexample implementations of block 602, the request data may also includean authenticated indication of the identity of the user.

As shown at block 604, process 600 continues with the extraction of arequest data set for the relevant inventory from the request dataobject. As discussed herein, the request data set may includeinformation sufficient to identify the relevant inventory, such as themobile devices and/or quantity of such devices to be distributed. Insome example implementations, the request data set includes a setadditional information, such as the information that may be availablefrom any of the data warehouses or other repositories discussed hereinand/or other information related to the request itself.

In block 606, the process 600 involves the receipt of a series ofcontext data objects. The context objects received in block 606 mayinclude any of the data sets discussed and/or otherwise contemplatedherein, including but not limited to the data sets that may be stored inone or more memories, data warehouses, and/or other data repositories.As set out in process 600, example implementations of block 606 involvecontext data objects and data sets associated with resources andchannels through which such resources may be delivered and/or otherwisedistributed. This data is used to drive the predictive model used toidentify and model the channel and conditions that will allow for theefficient distribution of the relevant inventory and/or other resourcesat a time in the future.

As shown in block 608, process 600 also includes using a machinelearning model (such as through the application of the received contextdata objects and data set, for example) to generate and/or otherwiseretrieve a predicted channel and/or condition set. In some exampleimplementations, the model may be a MARS model, and, upon theapplication of the relevant data sets to the model, one or more channelsand the relevant conditions (such as pricing, capacity, and/or the like,for example) are predicted and modeled so as to identify channels andconditions that will allow for the efficient distribution of therelevant resources.

As shown at block 610, process 600 also includes causing a renderableobject with the predicted channel and condition data set to be displayedon a user device. In some example implementations, the renderable objectmay be transmitted to a user device and cause the channel information,condition information, and/or other content contained in the predictedchannel and condition data set to be presented to the user in a mannerthat allows the user to view and interact with the information.

Example Contextual Implementation for Channel Prediction and Modelling

As noted herein, some example implementations arise in contextsinvolving the resale of mobile devices received in connection with thefulfillment of insurance programs and/or other service contracts. Insome such situations, mobile devices are directed to one or moreaggregators that are capable of distributing mobile devices throughvarious commercial channels. In connection with identifying andselecting the aggregators (or other channels) to which to direct one ormore sets of mobile devices, numerous categories of disparate data arecaptured, scaled, and/or otherwise processed to allow for thealgorithmic tiering of aggregators and the direction of resources tothose aggregators.

In some example implementations, several processes are involved withidentifying the available inventory to be distributed, receiving bidsfrom aggregators for portions of that inventory, algorithmically tieringthe aggregators, developing and applying a decay curve to the offersassociated with the aggregator bids, determining the optimal offer andprofit calculation from amongst the offers from the aggregators, andallocating the inventory amongst the available channels. These processestend to occur in a periodic cycle (such as weekly, monthly, and/or onanother periodic schedule. FIG. 7 is a flow chart of depicting anexample process 700 that reflects these and other operations inaccordance with some example implementations that may be used in theallocation of resources to aggregators.

As shown at block 702, the example process 700 includes acquiringresource inventory and one or more offers from aggregators. In a givencycle, upon receiving a list of the available inventory and/or expectedinventory to be distributed, the system shares all or part of theavailable inventory information with the relevant group of aggregators.Based on this available inventory information, each aggregator preparesan offer, which may take the form of a request data object that containsa plurality of request parameters, such as the bid price for one or moreSKUs, expected margin information, the quantities of the variousinventory elements that the aggregator requests, and/or otherinformation requested as part of the bid process, for example. In someexample implementations, additional information about the aggregator maybe supplied by the aggregator and/or determined by the system to build achannel profile, which reflects a set of properties associated with agiven aggregator.

As shown at block 704, example process 700 includes updating therelevant decay parameters and the relevant tiering parameters. Inaddition to soliciting bids and/or other resource requests from theaggregators, the system updates the bid decay parameters and tieringparameters in advance of calculating tiers to which the aggregators areassigned and a decay curve to be applied to the collected bids. In someexample implementations, the set of tiering parameters and/or the set ofdecay parameters may be received by the system as data objects, fromwhich the relevant parameters may be extracted for use by the system.

Since the categories of disparate data used in connection with exampleimplementations may come from multiple independent sources and/or mayreflect quantities, values, and/or other metrics that are set atmultiple different scales, one of the data processing steps used inconnection with the algorithmic tiering of aggregators involves scalingthe data, which may, for example result in a transformed numericalvalue, such as a limited set of integers, a scaled range of values, orthe like, for example, that can be more readily combined and processed.

One of the factors that may be used in connection with tiering one ormore aggregators is the volume, at a portfolio level, offered by a givenaggregator. In some example implementations, the relevant volume is thesum of all volumes offered by an aggregator across all of the relevantskus for which the aggregator may be used to distribute. It will beappreciated that such a volume measurement can provide a gauge into theoverall volume of mobile devices and/or other resources that theaggregator intends to buy, and further indicates the scale of businessthat the aggregator can provide. Since the information underlying thevolume calculation is typically expressed as a actual number of unitsfor each relevant sku, a scaled value may be achieved through the use ofa scoring algorithm that ranks each aggregator based on their totalexpressed volume, assigns the highest score to the aggregator with thehighest rank, and assigns incrementally lower scores to the otheraggregators based on their rank.

Another factor that may be used in connection with tiering one or moreaggregators is the portfolio-level profit margin projected by one ormore aggregators. In some example implementations, the portfolio-levelprofit margin is calculated by summing the profit margin across all SKUsidentified by a given aggregator. It will be appreciated that thisaggregated, portfolio-level profit margin is representative of the totalprofit margin that may be available via a given aggregator. Since theinformation underlying the portfolio-level profit margin is typicallyexpressed as a monetary value, a scaled value may be achieve through theuse of a scoring algorithm that ranks the aggregators based on theirrespective profit margins, assigns the highest score to the aggregatorwith the highest projected margin, and assigns incrementally lowerscores to the other aggregators based on their rank.

Another factor that may be used in connection with tiering one or moreaggregators is the entropy, or measure of variety associated with agiven aggregator. In some example implementations, the entropymeasurement reflects the variability offered by an aggregator on thevarious unique SKUs that the aggregator is associated with. Thisinformation provides insight into how many types of devises anaggregator intends to buy and indicates the scale of business theaggregator is capable of providing. In one example implementation, anentropy measurement is determined and scaled by sorting the relevantCNNs according to their respective revenue potentials by multiplying theCNN volumes by their predicted average selling price. The sorted devicesare then combined, or binned, into categories (such as ten categories,for example) according to their ranks. An aggregator-level entropymeasurement can then be obtained using the formula E=Σn*log n, where nis the volume of devices bid in a given bid, divided by the total volumeof devices bid. Entropy scores are then assigned an integer score basedon an inverse ranking of entropy values. As such, a higher entropy scoreof an aggregator tends to signify that the aggregator is bidding on alarge number of CNNs, and there is a potentially better customer to theprovider of mobile devices from a perspective focused on variability.

Another factor that may be used in connection with tiering one or moreaggregators is the specialty of the aggregator. In some exampleimplementations, the identification of the geographic market in which anaggregator focuses its efforts is relevant to determining the extent towhich the aggregator competes in a given market. For example, if aspecialty in a domestic market would tend to unduly increasecompetition, the geographic focus of an aggregator may be assigned on apoint scale that incorporates positive and negative numbers, such as theintegers from −2 through 2, where a domestic-only aggregator received an−2, a mostly domestic aggregator received a −1, an aggregator with anequal domestic and international footprint received a zero, a mostlyinternational aggregator received a 1, and a wholly internationalaggregator received a 2.

Another factor that may be used in connection with tiering one or moreaggregators is the length of the relationship between the source of themobile devices and/or other resources and the aggregator. In somesituations, the length of the relationship tends to correlate to thestability of the business relationship. To translate a temporalmeasurement into a scaled value, a scoring algorithm may be used thatcalculates the length of the relevant relationship in days, and thensupplies an inverse ranking to ensure that the longest relationshipreceive the most points.

Another factor that may be used in connection with tiering one or moreaggregators is the determination of whether an aggregator has failed arelevant audit. In some situations, the failure of an audit wouldindicate that an aggregator should be subjected to additional scrutinyand/or a penalty before allocating mobile devices and/or other resourcesto the aggregator. Since the failure of an audit is a binary condition,a scoring algorithm may be used to apply a binary score to a givenaggregator, such as a −1 score for an aggregator that failed an audit,and a zero score for a non-failing aggregator.

Another factor that may be used in connection with tiering of one ormore aggregators is the invoice amount each business-to-businessaggregator provided in a previous offer cycle. In some situations, aprevious invoice amount is an indicator of the actual amount of businessprovided by a given aggregator, as opposed to a predicted levelreflected in an offer or bid. Since the invoice amount is typicallyexpressed in its native form as a monetary value, the invoice value maybe scaled through the application of an algorithm that identifies thehighest total invoice amount amongst a set of aggregators, provides thehighest score to that aggregator, and then incrementally decreases thescore applied to lower-ranked aggregators.

Other factors that may be used in connection with tiering one or moreaggregators include ranking based on D&B Paydex scores, the extent towhich the aggregator has an exclusive relationship with the source ofmobile devices and/or other resources, the value and/or volume of returnmaterial authorizations sought by an aggregator over time, thetimeliness of bids, and the timeliness of payments, for example.

As shown in block 706, example process 700 includes applying the tieringparameters to the relevant aggregators. Regardless of the precisefactors used to generate scaled scores for use in tiering a group ofaggregators, upon the compilation of scores, the aggregators may beautomatically divided into multiple tiers. For example, based on scoresbuilt up over four or more (or another number, for example) biddingcycles, one set of aggregators may be assigned to the highest tier,while lower ranked aggregators may be assigned to lower tiers. Forexample, the top 40% of aggregators may be assigned to tier 1, the next30% of aggregators may be assigned to tier 2, and the remaining 30% maybe assigned to a lower, third tier. Regardless of the particularthresholds applied, the channel profile associated with an aggregator isassigned to a tier based at least in part on the application of one ormore of the tiering parameters discussed herein and/or additionalinformation received in a bid from an aggregator to a tiering algorithm.In some example implementations, the tier assigned to a given aggregatoris used in connection with further algorithmic assessment of the bid orbids provided by a given aggregator and/or the allocation of inventoryacross a set of qualified aggregators.

One of the technical challenges that arises in example implementationsthat deal with the distribution of resources is the potential for highvolatility in market and spot prices, which is further compounded by thepotential for third-party data sets and other data sources to includeerrors. Since machine learning algorithms tend to be sensitive to therange and distribution of attribute values, addressing outlier data canbe important in avoiding situations where the training process is undulyextended or altered due to outlier data. In some situations, such aswhere multiple data points are captured relatively closely in time, itmay be advantageous to delete an outlier data point (such as a pricingvalue and/or rate-of-change value) that falls outside the expectedrange. In some situations, such as when data points are sparse, it maybe advantageous to replace an outlier value with an interpolationbetween two or more adjacent data points, a weighted average, and/or amoving average.

As shown at block 708, the example process 700 includes applying a decaycurve to offers received from one or more aggregators. As noted herein,in addition to assigning one or more aggregators to a given tier basedon certain scaled parameters, example implementations furthercontemplate the application of a decay curve to the bid and/or bidsassociated with a given aggregator. As noted herein, some aspects of theassessment of aggregators, comparing bids received from aggregators, andallocating resources to various channels involve the use of multiplesets of data acquired over time. In order to prevent aged data fromobscuring current trends and/or otherwise impairing the predictive powerof the relevant model or models, some example implementationscontemplate the use of a decay factor that is applied to bids and/oraged data to reduce the impact the aged data has over time and to ensurethat the model retains its power to predict future pricing informationand/or other information bearing on the ability to efficientlydistribute inventory via one or more channels. One approach todeveloping a decay factor and/or otherwise processing the relevant datainvolves the use of a MARS (multivariate adaptive regression splines)model. MARS is a non-parametric regression technique (which some view asan extension of linear models) that automatically models nonlinearitiesand interactions between variables. In general, MARS build models of theform:

{circumflex over (f)}(x)=Σ_(i=1) ^(k) c _(i) B _(i)(x)

In accordance with such a form, the model is a weighted sum of basisfunctions B_(i)(x), where each c_(i) is a constant coefficient. In sucha model, each basis function may take one of the following forms: (1) aconstant 1, where there is one such term, the intercept; (2) a hingefunction that has the form max(0, x−const) or max(0, const−x), and whereMARS automatically selects variables and values of those variables forknots of the hinge function; or (3) a product of two or more hingefunctions, which can model the interaction between two or morevariables. As such, through the application of the various model outputsand/or intermediate signals, a decay rate and/or decay function can bedetermined, incorporated into a larger model, and applied to one or moresets of data.

In some situations, the application of a MARS-based model to establish adecay function allows for pricing information provided by an aggregatorand/or otherwise obtained (such as through the analysis of sales on adistributed user platform, such as eBay™ and/or other channels throughwhich mobile devices may be directly sold to consumers, for example) tobe fed to the decay model such that the prices at which customers arelikely to purchase the mobile devices at a particular time in the futurecan be modeled. For example, the pricing estimates obtained fromaggregators and market pricing information can be fed as inputs to theMARS-based model, along with other scaled data streams (such as thosereceived in connection with the assessment of aggregators, included in abid, and/or additional market data, for example) to create pricingcurves that predict the likely decay in pricing for a given mobiledevice SKU over time. Using the combination of the tiered ranking ofaggregators and the predicted decayed pricing curves, mobile deviceinventory can be directed to the aggregators that are most likely to beable to distribute the mobile devices at the time when the devicesactually become available.

As noted herein, some example implementations arise in contexts whereused mobile devices and/or other resources are acquired through buybackprograms, insurance contracts, and/or other arrangements that prevent acentral actor from having total control over the content and volume ofthe acquired inventory. However, the information used to tier theaggregators (such as the bids, expected pricing, and expected profitmargin information, for example), coupled with the predicted pricingdecay curves acquired through the use of the MARS-based model, can becombined and applied to a logistic regression model to set the pricesand/or range of prices at which inventory can be appropriately acquired.This can be particularly advantageous in situations where inventory thatis not being effectively distributed via one channel can be redirectedto an alternate channel with capacity.

For example, the MARS-based model develops a pricing decay function thatprovides, as output, the predicted price for all of the relevant mobiledevices and/or other resources for a given time window in the future.This pricing information can then be combined with the tieringinformation and a list of all devices that are to be allocated. Forexample, the pricing information provided in bids from aggregators,additional market data, data defining internal margin guidelines, andthe like can be combined with the anticipated future pricing tocalculate a price at which each available item in inventory is likelysell at during an interval of time in the future.

After the pricing decay function has been applied to the bids receivedfrom the aggregators, the system generally holds three categories ofinformation that can be combined and otherwise applied to a model toidentify the optimal offer(s) and profit(s) for the available inventory.This information, including but not limited to the results of thetiering and the application of the decay curve to any relevant offers,may be held in memory, as shown in FIG. 7 at block 710

As shown in blocks 712 and 714, the example process 700 includesdetermining one or more optimal offers, determining a resourceallocation, and applying the resource allocation. It will be appreciatedthat not all aggregators, other channels, and their respective bids arecreated equal. As noted herein, a number of different data points arecombined in connection with assigning a tier to an aggregator. Inaddition to the tiering approach, the system and/or other central actormay engage in different relationships bounded by different rules and/orother thresholds that govern at least a portion of the allocation ofinventory. For example, one or more aggregators may be internal partnerswith the central actor and/or may have a whole or partial exclusivityarrangement that entitles the aggregator to at least a portion of theinventory regardless of the competitiveness of its bid and/or the tierinto which the aggregator is assigned. In some example situations, suchparticularized relationships may be sufficient to allocate all or mostof the available inventory.

In situations where inventory remains available after the rulesassociated with any particularized relationships are fulfilled, the bidsfrom the aggregators are ranked. In some example implementations, theranking may be performed across all aggregators for a given set of itemsin inventory. In other example implementations, the bids may be furthersubdivided based on the quantity requested in connection with the bidprior to ranking. After the bids have been ranked, one or morethresholds are applied in some example implementations to limit thenumber of bids under consideration. For example, a threshold may be setat three bids (or some other number of bids) such that the top three(and/or the group of aggregators submitting the three highest bids) areconsidered to have satisfied the threshold. In some such exampleimplementations, the tier in which a high-bidding aggregator is assignedis considered to exclude bidders from disfavored tiers and/or includebidders from preferred tiers.

Upon identifying the three (or more) aggregators with the highest bids,the relevant resource inventory is allocated to the channel profiles.Based on the decayed pricing curve, the generated probabilities, and thecalculated profitabilities associated with the highest bids thatotherwise satisfy the relevant thresholds, an offer price for eachrelevant item in inventory is generated. This offer price is then usedto calculate a profit margin from the perspective of the central actor,and, where the margin is positive, the inventory items can bealgorithmically allocated based on the calculated margin, which mayfurther be informed by the quantities requested by a given aggregator,and/or any of the intermediate calculations (such as the probabilitycalculations) referenced herein. It will be appreciated that in somesituations, other factors may be used in allocating the resources,particularly where considerations such as channel profile participation,perceptions of fairness, and/or other factors are permitted to have abearing on allocations. For example, an allocation frequency attributemay be calculated by determining a ratio between the number of times acertain channel profile has submitted a bid and the number of time thatthe channel profile has received an allocation. In other examplesituations, a crowd and/or oracle may be consulted to adjustallocations.

Additional steps may also be performed. For example, after the inventoryhas been initially allocated in accordance with the tiering ofaggregators and the predicted decayed pricing curves, requests byaggregators for additional inventory for distribution (along with bidsfor that inventory) are received and considered. The demand for givenSKUs and/or other inventory items is identified and scaled, while thebid pricing received from the aggregators is extracted for the availableadditional inventory. Based on the bid pricing and requested inventoryof the bids, the aggregator bids are re-ranked, such that the best bidsfor the additional inventory (which may be different than the initialrankings acquired through the tiering process described above, forexample). In some example implementations, the bids are applied toanother model that generates a set of probabilities that a givenaggregator will be able to distribute a given allocation, and furthermultiplies the generated probabilities against the profitabilityassociated with the highest bids.

In some example implementations, the various parameters associated withone or more channel profiles may vary, such that a direct comparison ofone parameter to another may be inappropriate in situations where such acomparison is desired. For example, one channel profile may include abid or other offer that is structured to be valid for 30 days or more,while a second channel profile may include a bid or other offer that isonly structured to be open for one week. In such situations, the decaycurve may need to be selectively applied and/or have timingconsiderations imposed to ensure that bids of different time durationsare decayed such that the bids can be compared in a similar time window.For example, if one channel profile provides a bid that is valid for anentire month, while a second channel profile provides a series of bidson a weekly basis, the first weekly bid may be decayed over the courseof a month to allow for an accurate comparison to the month-long bid. Insuch an example, the second weekly bid may be decayed for three weeks,the third weekly bid may be decayed for two weeks, and the fourth weeklybid may be decayed for one week.

Based on the available inventory and the underlying aggregatorinformation, the probability that a given aggregator would acceptupdated terms associated with the additional inventory is calculated andused to set an offering price at which additional inventory may beacquired on the market and/or bought back from aggregators withslow-moving inventory. As such, discrepancies between the calculateddevice allocations based on the initial tiering and price decay modelscan be addressed through the redirection of inventory and/or theacquisition of additional inventory to satisfy demand in a given channelthat exceeds the initial allocation.

Example System Environment for Resource Offer Generation

FIG. 8 shows another example system environment 800 in whichimplementations involving improved resource offer generation may berealized. The depiction of environment 800 is not intended to limit orotherwise confine the embodiments described and contemplated herein toany particular configuration of elements or systems, nor is it intendedto exclude any alternative configurations or systems for the set ofconfigurations and systems that can be used in connection withembodiments of the present disclosure. Rather, FIG. 8 and theenvironment 800 disclosed therein is merely presented to provide anexample basis and context for the facilitation of some of the features,aspects, and uses of the methods, apparatuses, and computer programproducts disclosed and contemplated herein. It will be understood thatwhile many of the aspects and components presented in FIG. 8 are shownas discrete, separate elements, other configurations may be used inconnection with the methods, apparatuses, and computer programsdescribed herein, including configurations that combine, omit, and/oradd aspects and/or components. For example, in some embodiments, theresource offer generation system 802 may be partially or entirelycombined with the prediction system 102 to form a single componentconfigured to perform the operations disclosed herein with respect toboth systems.

Embodiments implemented in a system environment such as systemenvironment 800 advantageously provide, in addition to the efficientprediction and modeling of conditions and channels through whichresources may be distributed, as described above, improved resourceoffer generation associated with a given region-program identifier bypreparing and/or retrieving one or more resource offer generation inputdata sets, and/or an expected resource volume data set, an averagedistribution term data set, and a market intelligence data set,generating a fair market offer set based on the retrieved data setsusing an exception detection model, receiving a benchmark and portfoliotarget data set, and generating a resource offer set by applying one ormore of the retrieved and generated data sets to a resource offergeneration model, and/or sub-models therein. Some such implementationscontemplate rendering an offer adjustment interface to a client deviceassociated with an offer control user, where the offer adjustmentinterface is configured to receive manual adjustments to the generatedresource offer set for updating to create an adjusted resource offerset. Further, some such embodiments cause rendering of an approvalinterface to an approval device associated with an offer approval user,where the interface is configured for improved analysis of the adjustedresource offer set or generated resource offer set, and approval orrejection of the adjusted resource offer set or generated resource offerset. Some such embodiments leverage a hardware and software arrangementor environment for improved resource offer generation via the actionsand operations described, contemplated, and/or otherwise disclosedherein.

As shown in FIG. 8, the system 800 may include a prediction system 102,request source system 104, and content system 106. These components mayeach function similarly to perform the operations described above withrespect to FIG. 1. For example prediction system 102 may be includeprediction system module 102A configured to receive, process, transform,transmit, communicate with and evaluate request data objects, channelcontext data objects, the content and other information associated withsuch data objects, other data sets, and related interfaces via a server,such as prediction system server 102B or prediction system device 102D,prediction system database 102C configured to store informationassociated with request data objects, users, resources (such as usedmobile devices, for example) and/or channels associated with requestdata objects, channel context data objects, other data sets, interfacesassociated with any such data objects or data sets, request sourcesystems, channel content systems, and/or any other information relatedto the efficient prediction and modeling of conditions and channelsthrough which resources may be distributed and the generation of one ormore related messages and/or digital content item sets, and predictionsystem device 102D configured to provide an additional means (other thanvia a user interface of the prediction system server 102B) to interfacewith the other components of prediction system 102 and/or othercomponents shown in or otherwise contemplated by system environment 100.The prediction system server 102B and/or prediction system device 102Dmay connect the prediction system via any of a number of public and/orprivate networks, including but not limited to the Internet, the publictelephone network, and/or networks associated with particularcommunication systems or protocols, and may include at least one memoryfor storing at least application and communication programs.

Similarly, system environment 800 also includes content system 106,which comprises a content module 106A, a content server 106B, and acontent system database 106C, where the content system 106 is configuredfor communicating with prediction system 102 to provide information thatthe prediction system 102 may need when predicting and modelingconditions and channels through which resources may be distributed.Additionally, system environment 800 includes request source system 104,which may originate one or more request data objects, request dataobject information and/or additional content or other information to beassociated with one or more request data objects.

System environment 800 further includes resource offer generation system802. The resource generation system 802 comprises resource offergeneration system module 802A, resource offer generation system server802B, and resource offer generation system database 802C. The resourcegeneration system module 802A may be configured to receive, process,transform, transmit, communicate with, and evaluate offer requests,resource offer data objects, the content and other informationassociated with such data objects, other data sets, and relatedinterfaces, to generate a resource offer set, facilitate adjustment of aresource offer set, and/or manage approval of submitted resource offersets. The resource offer generation system module 802A may perform theseoperations, and/or additional or alternative operations, via a server,resource offer generation system server 802B, or corresponding device.The resource offer generation system server 802B may be connected to anynumber of public and/or private networks, including but not limited tothe Internet, the public telephone network, and/or networks associatedwith particular communication systems or protocols, and may include atleast one memory for storing at least application and communicationprograms.

Resource offer generation system 802 also includes a resource offergeneration database 802C that may be used to store informationassociated with offer requests, users, resources (such as used mobiledevices, for example), and/or offer requests or correspondinginformation associated with offer requests, region-program data objectsor corresponding information, resource offer sets, adjusted resourceoffer sets, other data sets, interfaces associated with the offerrequests, and/or any other information related to improved generation ofresource offer set(s). The resource offer generation system database802C may be accessed by the resource offer system module 802A and/or theresource offer system 802B. In some embodiments, the resource offergeneration system database 802C may, additionally or alternatively, beaccessed by the prediction system module 102A, prediction system server102B, and/or prediction system device 102D, to store informationreceived by, generated by, or accessed by the components of theprediction system 102. While FIG. 8 depicts resource offer generationsystem database 802C as a single structure, it will be appreciated thatgeneration system database 802C may additionally or alternatively beimplemented to allow storage in a distributed fashion and/or atfacilities that are physically remote from each other and/or the othercomponents of offer generation system 802. Additionally oralternatively, in some embodiments, some or all of the prediction systemdatabase 102C and the resource offer generation system database 802C maybe embodied as a single, joint database or distributed repository.

It will be appreciated that all of the components shown FIG. 8 may beconfigured to communicate over any wired or wireless communicationnetwork including a wired or wireless local area network (LAN), personalarea network (PAN), metropolitan area network (MAN), wide area network(WAN), or the like, as well as interface with any attendant hardware,software and/or firmware required to implement said networks (such asnetwork routers and network switches, for example). For example,networks such as a cellular telephone, an 802.11, 802.16, 802.20 and/orWiMax network, as well as a public network, such as the Internet, aprivate network, such as an intranet, or combinations thereof, and anynetworking protocols now available or later developed including, but notlimited to TCP/IP based networking protocols may be used in connectionwith system environment 100 and embodiments of the invention that may beimplemented therein or participate therein.

Overall, and as depicted in system environment 800, in addition to theprocesses and operations facilitated and described with respect to thesystems 102-106, resource offer generation system 802 engages inmachine-to-machine communication with request source system 104,prediction system 102, and context content system 106, via one or morenetworks, to facilitate the processing of offer requests received from auser, improved generation and management of resource offer sets andcorresponding resource offer data objects, and the generation and/ortransmission of control signals for causing rendering of interfaces forviewing the resource offer set and/or offer analytics data set and/ormarket information, adjusting the resource offer set, and/or approvingsubmitted adjusted resource offer sets.

Example Apparatus for Implementing Improved Resource Offer Generation

It will be appreciated that the resource offer generation system 802 maybe embodied by one or more computing systems, such as apparatus 900shown in FIG. 9. As illustrated in FIG. 9, the apparatus 900 may includea processor 902, a memory 904, input/output circuitry 906,communications circuitry 908, data management circuitry 910, and modelperformance circuitry 912. The apparatus 900 may be configured toexecute any of the operations described herein.

Regardless of the manner in which the apparatus 900 is embodied, theapparatus of an example embodiment is configured to include or otherwisebe in communication with a processor 902 and a memory device 904 andoptionally the input/output circuitry 906 and/or a communicationscircuitry 908. In some embodiments, the processor (and/or co-processorsor any other processing circuitry assisting or otherwise associated withthe processor) may be in communication with the memory device via a busfor passing information among components of the apparatus. The memorydevice 904 may be non-transitory and may include, for example, one ormore volatile and/or non-volatile memories. In other words, for example,the memory device may be an electronic storage device (e.g., a computerreadable storage medium) comprising gates configured to store data(e.g., bits) that may be retrievable by a machine (e.g., a computingdevice like the processor). The memory device may be configured to storeinformation, data, content, applications, instructions, or the like forenabling the apparatus to carry out various functions in accordance withan example embodiment of the present disclosure. For example, the memorydevice could be configured to buffer input data for processing by theprocessor. Additionally or alternatively, the memory device could beconfigured to store instructions for execution by the processor.

As described above, the apparatus 900 may be embodied by a computingdevice. However, in some embodiments, the apparatus may be embodied as achip or chip set. In other words, the apparatus may comprise one or morephysical packages (e.g., chips) including materials, components and/orwires on a structural assembly (e.g., a baseboard). The structuralassembly may provide physical strength, conservation of size, and/orlimitation of electrical interaction for component circuitry includedthereon. The apparatus may therefore, in some cases, be configured toimplement an embodiment of the present disclosure on a single chip or asa single “system on a chip.” As such, in some cases, a chip or chipsetmay constitute means for performing one or more operations for providingthe functionalities described herein.

The processor 902 may be embodied in a number of different ways. Forexample, the processor may be embodied as one or more of varioushardware processing means such as a coprocessor, a microprocessor, acontroller, a digital signal processor (DSP), a processing element withor without an accompanying DSP, or various other processing circuitryincluding integrated circuits such as, for example, an ASIC (applicationspecific integrated circuit), an FPGA (field programmable gate array), amicrocontroller unit (MCU), a hardware accelerator, a special-purposecomputer chip, or the like. As such, in some embodiments, the processormay include one or more processing cores configured to performindependently. A multi-core processor may enable multiprocessing withina single physical package. Additionally or alternatively, the processormay include one or more processors configured in tandem via the bus toenable independent execution of instructions, pipelining and/ormultithreading.

In an example embodiment, the processor 902 may be configured to executeinstructions stored in the memory device 904 or otherwise accessible tothe processor. Alternatively or additionally, the processor may beconfigured to execute hard coded functionality. As such, whetherconfigured by hardware or software methods, or by a combination thereof,the processor may represent an entity (e.g., physically embodied incircuitry) capable of performing operations according to an embodimentof the present disclosure while configured accordingly. Thus, forexample, when the processor is embodied as an ASIC, FPGA or the like,the processor may be specifically configured hardware for conducting theoperations described herein. Alternatively, as another example, when theprocessor is embodied as an executor of software instructions, theinstructions may specifically configure the processor to perform thealgorithms and/or operations described herein when the instructions areexecuted. However, in some cases, the processor may be a processor of aspecific device (e.g., a pass-through display or a mobile terminal)configured to employ an embodiment of the present disclosure by furtherconfiguration of the processor by instructions for performing thealgorithms and/or operations described herein. The processor mayinclude, among other things, a clock, an arithmetic logic unit (ALU) andlogic gates configured to support operation of the processor.

In some embodiments, the apparatus 900 may optionally includeinput/output circuitry 906, such as a user interface that may, in turn,be in communication with the processor 902 to provide output to the userand, in some embodiments, to receive an indication of a user input. Assuch, the user interface may include a display and, in some embodiments,may also include a keyboard, a mouse, a joystick, a touch screen, touchareas, soft keys, a microphone, a speaker, or other input/outputmechanisms. Alternatively or additionally, the processor may compriseuser interface circuitry configured to control at least some functionsof one or more user interface elements such as a display and, in someembodiments, a speaker, ringer, microphone and/or the like. Theprocessor and/or user interface circuitry comprising the processor maybe configured to control one or more functions of one or more userinterface elements through computer program instructions (e.g., softwareand/or firmware) stored on a memory accessible to the processor (e.g.,memory device 904, and/or the like).

The apparatus 900 may optionally also include the communicationcircuitry 908. The communication circuitry 908 may be any means such asa device or circuitry embodied in either hardware or a combination ofhardware and software that is configured to receive and/or transmit datafrom/to a network and/or any other device or module in communicationwith the apparatus. In this regard, the communication interface mayinclude, for example, an antenna (or multiple antennas) and supportinghardware and/or software for enabling communications with a wired and/orwireless communication network. Additionally or alternatively, thecommunication interface may include the circuitry for interacting withthe antenna(s) to cause transmission of signals via the antenna(s) or tohandle receipt of signals received via the antenna(s). In someenvironments, the communication interface may alternatively or alsosupport wired communication. As such, for example, the communicationinterface may include a communication modem and/or otherhardware/software for supporting communication via cable, digitalsubscriber line (DSL), universal serial bus (USB) or other mechanisms.

As shown in FIG. 9, the apparatus may also include data managementcircuitry 910. The data management circuitry 910 includes hardwareconfigured to retrieve, receive, generate, or otherwise accessinformation and data for use in generating a fair market offer set,generating a resource offer set, and/or optimizing a resource offer set.For example, the data management circuitry 910 may access one or morelocal and/or remote databases to create, retrieve, or otherwise preparea base table for use by one or more models. The base table may beassociated with one or more stored tables, data sets, or the like,comprising inputs to one or more models, such as a resource offergeneration model. In some embodiments, the data management circuitry 910is configured to prepare, such as via a base table associated with oneor more database, one or more resource offer generation input data sets,including resource offer generation input data sets may include ahistorical offer data set, a resource list data set, a marketintelligence data set, and a resource mapping data set.

The data management circuitry 910 may be configured to retrieve, access,or create, a mapping of various resource identifiers associated withvarious third-party entities, aggregators, and the like, to astandardized resource set identifier, such as a CNN. For example, usingthe example of used mobile phones as a resource, a used mobile phonehaving the same attributes or specification (e.g., carrier, memory size,model, make) may be associated with a different resource identifier fora first third-party entity and a second third-party entity. The resourcemapping may be performed automatically, manually, or with a combinationof both automatic and manual steps for mapping third-party resourceidentifiers to the standardized resource set identifier, such as a CNN.The mapping may be stored as a resource mapping data set in a databaseor repository.

In some embodiments, the data management circuitry 910 may receive,obtain, and/or prepare market intelligence data from variousthird-parties. A received market intelligence data set may be associatedwith a third-party system, and require standardization and/orsanitization for use by one or more data models using a resource mappingdata set. The data management circuitry 910 may include means configuredto perform one or more processing algorithms on a received marketintelligence data set before storing it for use by one or more models.

In some embodiments, the data management circuitry 910 may receive,obtain, prepare, and/or otherwise access an expected resource volumedata set. The expected resource volume data set may include storedchannel profiles where resources are to be distributed as allocatedanother system, such as a prediction system 102. The expected resourcevolume data set may be generated by another system, such as a predictionsystem 102, and stored to a database accessible via the data managementcircuitry 910. For example, the expected resource volume data set may bea portion of the outputted data by a prediction system 102.

In some embodiments, the data management circuitry 910 may receive,obtain, prepare, and/or otherwise access an average distribution termdata set. The average distribution term data set may include at least anaverage sales price for various resources associated with variousresource set identifiers. In some embodiments, the average distributionterm data set may be generated by another system, such as predictionsystem 102 or another system configured to generate an averagedistribution data set based on the output of prediction system 102, andstored to a database accessible via the data management circuitry 910.

The data management circuitry 910 may provide an interface, such as anapplication programming interface (API), which allows other componentsof a system to obtain, generate, or otherwise access the various datasets. In some embodiments, the data management circuitry 910 may obtaininformation about one or more resources or economic factors associatedwith the distribution of resources. For example, the data managementcircuitry 910 may retrieve and/or standardize data associated withprevious distribution of similar resources by the system, distributionof the resource by one or more third-party entities (e.g., competitors),distribution of resources by neutral-competitor entities (e.g.,business-to-consumer competitors), macro-economic factors associatedwith a resource, promotion periods associated with distribution of aresource, and/or other information that may be used in generatingimproved resource offer generation, which includes but is not limited toany of the information that may be obtained from and/or otherwiseassociated with a content system 106.

The data management circuitry 910 may facilitate access to informationfor use by the one or more models for improved resource offer generationthrough the use of applications or APIs executed using a processor, suchas the processor 902. However, it should also be appreciated that, insome embodiments, the data management circuitry 910 may include aseparate processor, specially configured field programmable gate array(FPGA), or application specific interface circuit (ASIC) to manageretrieval, access, and/or use of the relevant data. The data managementcircuitry 910 may also provide interfaces allowing other components ofthe system to add, delete, or otherwise manage records to the resourceoffer generation system database 802C, and may also provide forcommunication with other components of the system and/or externalsystems (for example, a prediction system database 102C) via a networkinterface provided by the communications circuitry 908. The datamanagement circuitry 910 may therefore be implemented using hardwarecomponents of the apparatus configured by either hardware, software, ora combination of both hardware and software for implementing theseplanned functions.

The apparatus further includes model performance circuitry 912. Modelperformance circuitry 912 includes hardware, software, or a combinationthereof, configured to perform data validation for use with one or moremodels for improved resource offer generation, and maintain, utilize,and apply one or more models, such as algorithmic and/or machinelearning models, for improved resource offer generation. The modelperformance circuitry 912 may validate and/or receive and validate, acollection period data object received by a client device (for example,a request source system 104), a data collection parameter value set, andprerequisite data record sets retrieved and/or otherwise accessed, suchas utilizing data management circuitry 910, from an associated database.The model performance circuitry 912 may, additionally or alternatively,initiate a resource offer generation model, and/or apply one or morerelevant data sets to the resource offer generation model. In someembodiments, model performance circuitry may communicate with anexternal system and/or server (e.g., a cloud server), alone or inconjunction with one or more of the other components of the apparatus,which is configured to manage and perform the resource offer generationmodel and/or associated models and sub-models.

Example Processes for Resource Offer Generation and Adjustment

FIG. 10 illustrates an example data flow diagram 1000 for generating anoptimal resource offer set via a resource offer generation system. Thedata flow diagram 1000 includes data flow steps between components, suchas of an sub-systems of the system 800, including client device 1001,resource offer generation system 1003, and approval device 1005. Theclient device 1001 and admin device 1001 may each be embodied by arequest source system, such as a request source system 104. The clientdevice 1001 may be associated with an offer control user, such as anauthenticated user that authenticates and accesses the resource offergeneration system 1003 with permissions to originate offer requestsand/or adjust generated resource offer sets. Similarly approval device1005 may be associated with an offer approval user, such as anauthenticated user that authenticates and accesses the resource offergeneration system 1003 with permissions to review submitted adjustedresource offer sets. The resource offer generation system 1003 may beembodied by a resource offer generation system, for example the resourceoffer generation system 802 embodied by apparatus 900.

In data flow 1000, several steps illustrated may be optional. Optionalsteps are illustrated in FIGS. 10 and 11 in broken lines. In someembodiments, one or more of the optional steps may be performed. In someembodiments, all optional steps may be performed.

At optional step 1002, the client device 1001 may create and/orconfigure a region-program data object. In some embodiments, an offercontrol user (e.g., an analyst) may access an interface, via the clientdevice 1001, to create a new region-program data object. Eachregion-program data object may be associated with at least a regionidentifier (e.g., identifying a particular country or sub-region withina country), and a program identifier (e.g., identifying a particularoffering set within the country). The offer control user, via the clientdevice 1001 for example, may input one or more parameter valuesassociated with the region-program data object. For example, financialtarget parameters, pricing parameters, and/or business analyticsassociated with the particular region-program data object may beprovided by the offer control user. In some embodiments, an offercontrol user may identify and manage an existing region-program dataobject, for example by editing one or more parameter values for anexisting region-program data object.

At optional step 1004, the resource offer generation system 1003 maystore the configured region-program data object. The region-program dataobject may be configured by an offer control user at step 1002, andreceived by the resource offer generation system 1003 upon submission bythe offer control user via the client device 1001 (e.g., when the offercontrol user engages a save or submit button associated with theinterface for configuring the region-program data object). Theregion-program data object may be stored associated with a correspondingregion-program identifier. The region-program identifier may uniquelyidentify the region-program data object, and may be generated and/ordetermined by the resource offer generation system 1003.

At step 1006, client device 1001 may initiate resource offer generation.In some embodiments, the client device 1001 initiates resource offergeneration when the offer control user, via the client device 1001,selects an existing region-program data object for which the offercontrol user desires to generate a resource offer set. In someembodiments, the resource offer generation system 1003 may causerendering to the client device 1001 of an interface for selecting aregion-program data object for which the offer control user desires togenerate the resource offer set. For example, the resource offergeneration system 1003 may generate and/or transmit one or more controlsignals causing a renderable object comprising an interface rendered forselecting a region-program data object from a list of existingregion-program data objects.

At step 1008, the client device 1001 may submit a collection period dataobject, or corresponding collection period timestamps for defining acollection period data object, associated with the resource offer set tobe generated. In some embodiments, the collection period data object isdefined by, or includes, a collection period start timestamp and acollection period end timestamp. The collection period data object mayrepresent a time interval for which the offer control user is seeking toprovide resource offers based on the generated resource offer set. Insome embodiments, the offer control user may input the collection periodstart timestamp and the collection period end timestamp, for generatinga corresponding collection period data object, via an interface renderedto the client device 1001 (e.g., via a user interface component, such asa dropdown component, for inputting the collection start date timestampand the collection end date timestamp).

At step 1010, the resource offer generation system 1003 may receive aresource offer generation initiation request. The resource offergeneration request may be received in response to engagement, by theoffer control user via client device 1001 after input of the collectionperiod start timestamp and collection period end timestamp, of aninterface component configured to transmit the resource offer generationinitiation request. The resource offer generation initiation request maycause preparation of one or more resource offer generation input datasets for use by one or more models, such as a resource offer generationmodel and/or an exception detection model. In some embodiments, theresource offer generation initiation request comprises at least thecollection period start timestamp and collection period end timestampselected by the offer control user, which may be used by the resourceoffer generation system in creating and/or determining a collectionperiod data object. Alternatively, in some embodiments, the resourceoffer generation initiation request comprises a collection period dataobject created and/or transmitted from the a client device, for exampleclient device 1001.

At step 1012, the resource offer generation system 1003 may validate thecollection period data object. The resource offer generation system 1003may validate that the collection period start timestamp associated withthe collection period data object represents a future timestamp (e.g.,today or later). The resource offer generation system 1003 may alsovalidate that the collection period end timestamp associated with thecollection period data object represents another future timestamp withrespect to the collection period start timestamp (e.g., the collectionperiod end timestamp is the same date and/or time, or later, than thecollection period start timestamp). In this respect, the resource offergeneration system 1003 is configured to verify the collection periodrepresents a valid future timestamp range defined by the collectionperiod start timestamp and the collection period end timestamp.

Additionally or alternatively, the resource offer generation system 1003may validate that time interval represented by the collection perioddata object does not overlap with another collection period data objectfor an existing request or stored resource offer set. For example, theresource offer generation system 1003 may query a repository, such as anoffer approval repository, for all offer status records associated witha particular region-program identifier, where each offer status recordis associated with a collection period data object, and determine thateach of the collection period data objects for the stored offer statusrecords does not overlap the input collection period. The resource offergeneration system 1003 prevents multiple, conflicting

If the collection period data object is not validated at step 1014, theresource offer generation system 1003 may provide an error message tothe client device 1001. The error message may indicate that the selectedcollection period start timestamp and selected collection period endtimestamp are invalid (e.g., the timestamps do not form a valid datetimestamp range, or the interval embodied by the timestamp overlaps withanother collection period for a pending or existing resource offer set).The error message may, additionally or alternatively, prompt the offercontrol user of the client device 1001 to input a new collection periodstart timestamp and/or new collection period end timestamp. The errormessage may be configured for rendering, by the client device 1001, to acorresponding interface. If the collection period data object isvalidated at step 1014, flow continues to step 1016.

At step 1016, resource offer generation system 1003 may validate one ormore resource offer generation input data sets. The resource offergeneration input data sets may be retrieved from a repository, or aplurality of repositories, maintained by and/or accessible to theresource offer generation system 1003. The resource offer generationinput data sets may comprise data extracted from, or retrievedassociated with, a plurality of disparate resources and/or repositories,and/or data retrieved by various data extraction and/or retrievalprocesses. For example, in some embodiments, the resource offergeneration system 1003, and/or an associated system, generates and/orprepares one or more the resource offer generation input data sets viathe various data extraction and/or retrieval processes. In someembodiments, for example, some or all of one or more of the resourceoffer generation input data sets may be obtained via scraping one ormore tracked web resources, retrieval from public data repositories,retrieval from private data repositories, derived and/or tracked via theresource offer generation system 1003 and/or an associated system.

In some embodiments, the one or more resource offer generation inputdata sets are updated by the resource offer generation system (or anassociated system) at one or more predefined intervals. For example, theresource offer generation input data sets may be updated daily, weekly,or the like, or some resource offer generation input data sets may beupdated at a first interval and some resource offer generation inputdata sets may be updated at a second interval. Each of the resourceoffer generation input data sets may be associated with a particularregion-program identifier and/or a collection period data object.

Additionally or alternatively, one or more of the resource offergeneration input data sets may be updated in real-time. In someembodiments, one or more of the resource offer generation input datasets is updated automatically, in real-time, immediately prior tovalidation. In other embodiments, one or more of the resource offergeneration input data sets is updated in real-time when validation isunsuccessful for one or more of the resource offer generation input datasets.

In some embodiments, each of the one or more resource offer generationinput data sets is validated using one or more data sufficiency models.The data sufficiency models may determine whether the resource offergeneration input data sets satisfy one or more predeterminedrequirements. For example, a data sufficiency check may determinewhether one or more of the resource offer generation input data sets hasbeen updated within a predetermined time interval (e.g., updated withinthe previous day, the previous week, or the another time interval). Insome such embodiments, each of the resource offer generation input datasets may be associated with a last updated timestamp. Additionally oralternatively, in some embodiments, a data sufficiency check maydetermine whether one or more of the resource offer generation inputdata sets satisfies an expected accuracy threshold. For example, one ormore data accuracy models may be used to determine an accuracy value foreach of the resource offer generation input data sets based on expecteddata formats, missing data, and the like. It should be appreciated that,in other embodiments, additional or alternative data sufficiency checksmay be performed in any combination to validate the resource offergeneration input data sets.

The resource offer generation input data sets may be applied to one ormore models, such as algorithmic or machine learning models, for use ingenerating a resource offer set. The resource offer generation inputdata sets may be input, and/or otherwise utilized by, the one or moremodels for use in generating the resource offer set. For example, theresource offer generation input data sets may include one or more datasets for applying to an exception detection model to generate a trustedresource characteristic data set, for example a fair market offer set,which may be applied to or otherwise used by a resource offer generationmodel. Additionally or alternatively, the resource offer generationinput data sets may include one or more data sets for applying to aresource offer generation model to generate a resource offer set.

The resource offer generation input data sets may include a historicaloffer data set, a resource list data set, a market intelligence dataset, and a resource mapping data set. Each of the resource offergeneration input data sets may be obtained from a sub-system or deviceassociated with the resource offer generation system 1003. In someembodiments, the resource offer generation system 1003 may communicatewith one or more sub-systems and/or other associated devices to obtainthe resource offer generation input data sets via a database accessibleto resource offer generation system 1003. Each of the resource offergeneration input data sets may be stored in a separate table within adatabase accessible to the resource offer generation system 1003, and/ormay be stored associated with a base table linked to the tablescorresponding to the resource offer generation input data sets.

The resource offer generation input data sets may include a historicaloffer data set. The historical offer data set may include at leastinformation associated with previous generated offer data objects,resource acquisition information associated with the previous generatedoffer data objects, sales information associated with said resources, orthe like. The historical offer data set may be used to retrieve and/orgenerate an expected resource volume data set, which may be associatedwith particular resource set identifiers for a particular region-programidentifier.

The resource offer generation input data sets may include a resourcelist data set. The resource list data set may include resources presentin inventory for a particular region-program identifier and grade levelfor each resource set identifier associated with the resources. Theresource list data set may include warehouse details, which may compriseresource attributes, mapped to each resource set identifier forresources. For example, resource manufacture identifier and modelidentifier may be mapped to the resource set identifier and assigned aresource identifier. The resource list data set may also indicatewhether an resource offer data object should be generated for theresource in the resource list data set. For example, the resource listdata set may flag each resource for which a resource offer data objectis to be generated, for example using a bit flag.

The mapping data set may include correlation information for linkingvarious resource identifiers associated with various third-partyentities, aggregators, and the like, to a standardized resource setidentifier for use in analyzing the other resource offer generationinput data sets including data records retrieved from or associated witha third-party system. The mapping data set may be manually and/oralgorithmically created to map third-party resource identifiers to thestandardized resource set identifier for each region and/or third-party.For example, third-party resource identifier information may beretrieved associated with a particular third-party system for aparticular region. The third-party resource identifier information mayinclude one or more resource attribute values (e.g., a manufactureridentifier, model identifier, storage size identifier, and the like).The system may algorithmically provide a mapping between the third-partyresource identifier information and a standardized resource setidentifier, and generate a mapping score indicative of the likelihoodthe generated mapping is correct. The mapping score may be based onmatching of known resource attribute values associated with the resourceset identifier to resource attribute values parsed from the third-partyresource identifier information. The mapping score may then be comparedto a mapping confirmation threshold, wherein mapping scores that satisfythe mapping confirmation threshold (e.g., by exceeding the threshold)are deemed accurately mapped. If third-party resource identifierinformation is mapped and assigned a mapping score that does not satisfythe mapping confirmation threshold, the third-party resource identifierinformation may then be marked and/or otherwise caused to be reviewedfor manual mapping. The mapping data set may include the completedalgorithmic and manual mappings.

The resource offer generation input data sets may include a marketintelligence data set. The market intelligence data set may becollected, obtained, and/or otherwise retrieved from one or morethird-party systems. In some embodiments, the market intelligence datasystem may include information and/or data stored by the resource offergeneration system 1003, for example in a corresponding database. Theresource offer generation system 1003 may be configured to alter themarket intelligence data set using one or more processing algorithms forverifying the sufficiency and/or validity of the various records in themarket intelligence data set. For example, one or more processingalgorithms may be used to identify, and remove or flag for manualadjustment, data records not including required information for mappingthe market intelligence data to a resource set identifier. The marketintelligence data may be mapped as associated with a particular resourceset identifier and/or specific resources based on the mapping data set.For example, a particular subset of the market intelligence data set mayinclude or otherwise be associated with a resource set identifier towhich that subset of market intelligence data applies.

Additionally or alternatively, the resource offer generation input datasets may include one or more data sets derived from and/or generated bya prediction system as described herein. For example, the resource offergeneration input data sets may include an expected resource volume dataset, an average distribution term data set, The resource offergeneration input data sets may include a projected receipts data setderived from the expected resource volume set, average distribution termdata set, and/or other data objects or data sets derived from orgenerated by the prediction system. The projected receipts data set mayrepresent a projected resource volume for a given region-programidentifier, and/or may include an expected or predicted pricecharacteristic for each resource set identifier to be distributedassociated with a particular channel profile.

If the one or more resource offer generation input data sets are notvalidated at step 1018, an error message may be provided to the clientdevice 1001. The error message may indicate that one or more of theresource offer generation input data sets is/are not present in anassociated database, such as resource offer generation system database802C. For example, the resource offer generation input data sets may notbe validated when one or more of the prerequisite data record sets hasnot been generated for the region-program identifier for which theresource offer generation process was initiated at step 1006. In someembodiments, the resource offer generation system 1003 may query adatabase, for example embodied by the resource offer generation systemdatabase 802C, using the region-program identifier selected by the offercontrol user, to determine whether all required resource offergeneration input data sets exist and/or are updated as described above.If the resource offer generation input data sets are validated at step1018, flow may continue to step 1020.

At step 1020, the resource offer generation system 1003 may provide datacollection parameters to the client device 1001. The resource offergeneration system 1003 may identify the data collection parameters to beprovided based on the region-program data object configured by the offercontrol user and stored at an earlier step. For example, the collectionparameters may include particular financial analysis metrics, goals,costs, or other parameters associated with the region program dataobject. Some, none, or all of the collection parameters may beassociated with a default value configured by the offer control user atan earlier stage. Example collection parameters include a channel mixfor distribution channels, one or more activity costs, commissions,minimum offer requirements, minimum offer profit requirements, minimummargin requirements, volume mixes for resources, and/or multipliersand/or adjustment indices for resources based on one or more resourceattributes and/or conditions (e.g., a resource age multiplier, aresource functionality multiplier, a resource lock status multiplier,and the like).

The resource offer generation system 1003 may generate and/or transmitone or more control signals to the client device 1001, the controlsignal(s) causing a renderable data object comprising an interfacedisplayed at one or more client devices, including the client device1001. The interface may include components, or otherwise be configured,for rendering the data collection parameters for input by a user, suchas an offer control user associated with the client device 1001. Forexample, the interface may include an input component associated witheach data collection parameter for receiving a data collection parametervalue for the corresponding data collection parameter.

At step 1022, client device 1001 may render the data collectionparameters for input by an offer control user. In some embodiments, eachof the data collection parameters provided at step 1020 is rendered toan interface provided via the client device 1001 in response toreceiving the provided data collection parameters. For example, arenderable data object may be rendered by the client device 1001, wherethe renderable data object comprises an interface component for eachdata collection parameter. Each data collection parameter may berendered associated with an interface component for changing the valueassociated with the corresponding data collection parameter. Ifprovided, each data collection parameter may be rendered associated witha corresponding default value configured by the offer control user basedon the associated region-program data object. The user may engage withthe interface component associated with a data collection parameter toinput a new data collection parameter value associated with that datacollection parameter.

At step 1024, client device 1001 submits values for data collectionparameters. In some embodiments, the offer control user may engage aninterface component, such as a submit button, rendered to the interface,to submit the currently input values for the data collection parameters.The values submitted may include various values manually input and/orloaded by an offer control user via the client device 1001. In someembodiments, the values submitted may include one or more default valuesthat the offer control user did not change. The client device 1001 maytransmit an electronic data transmission including the data collectionparameter values to the resource offer generation system 1003, which mayreceive the electronic data transmission and parse the electronic datatransmission to extract the data collection parameter values.

At step 1026, resource offer generation system 1003 may receive and, insome embodiments, validate the submitted data collection parametervalues. The data collection parameter values may be validated using avalidation rule set stored by, and/or retrievable by, the resource offergeneration system 1003. The validation rule set may ensure that one ormore of the collection parameter values, alone or in combination withother collection parameter values, satisfies a predetermined rule. Forexample, in some embodiments, the validation rule set may includebusiness rules associated with the profitability and/or distributionallocation of resources.

If the data collection parameter values are not validated at optionalstep 1028, an error message may be provided to the client device 1001.The error message may indicate that one or more validation rules of thevalidation rule set were not satisfied. Additionally or alternatively,the error message may specifically indicate the particular validationrule not satisfied, and/or suggestions associated with alteringcollection parameter values to satisfy the validation rule set. If thedata collection parameter values are validated at optional step 1028,the data collection parameter values may be stored associated with theregion-program identifier and/or collection period data object submittedby the offer control user. In some embodiments, the data collectionparameter values may be stored as a benchmark and portfolio target dataset associated with the region-program data object. In some embodiments,the data collection parameter values may be stored in a temporary orstaging table of a database, such as the resource offer generationsystem repository 802C. Flow then continues to step 1030.

At step 1030, an offer control user requests resource offer generationvia the client device 1001. In some embodiments, the offer control usermay automatically request resource offer generation in response tosubmitting the collection parameter values. In other embodiments, uponsubmission and validation of the data collection parameter values, theoffer control user may be prompted, via the client device 1001, toprovide and submit an additional data set (e.g., values for one or moreadditional data collection parameters based on the originally submittedcollection parameter values).

To request resource offer generation, the client device 1001 maytransmit a resource offer generation request to the resource offergeneration system 1003. The resource offer generation request maycomprise, or otherwise be associated with, the region-program identifierassociated with the region-program data object initiated by the offercontrol user at an earlier step, and the collection period data object.Additionally, in some embodiments, the resource offer generation requestmay comprise the data collection parameter values for the various datacollection parameters.

At step 1032, the resource offer generation system 1003 may receive theresource offer generation request, for example transmitted by the clientdevice 1001. The resource offer generation system 1003 may receive theregion-program data object and/or corresponding region-programidentifier, and receive the collection period data object. The resourceoffer generation request indicates the offer control user has finalizedand submitted all parameter value inputs. In some embodiments, uponreceiving the resource offer generation request from the client device1001, the resource offer generation system 1003 may transfer the datacollection parameter values from the temporary or staging table to animplementation table accessible by one or more models for resource offergeneration. For example, in some embodiments, the data collectionparameter values may be transferred to an attributes table accessiblefor applying to a resource offer generation model and/or exceptiondetection model.

In some embodiments, the resource offer generation system 1003 maymaintain one or more repositories, databases, or the like, for managingoffer status records associated with resource offer sets generated for aparticular region-program identifier and collection period data object.For example, in some embodiments, the resource offer generation systemmay maintain an offer approval repository comprising an offer statusrecord for each region-program identifier and collection parameter dataobject for which resource offer generation has been requested. Eachoffer status record may be retrievable associated with theregion-program identifier and collection parameter data object.

An offer status record may be created upon receiving a resource offergeneration request. Once generated, the offer status record may beassociated with a requested status indicator. At any given time, anoffer status record for a particular region-program identifier andcollection period data object may be associated with only a singleresource offer set. The offer status record may first be associated withthe resource offer set generated at a later step. The resource offer setmay then be updated, or otherwise adjusted, to create an adjustedresource offer set, which then may be stored associated with the offerstatus record. Further, adjustments may be made to the stored resourceoffer set associated with the offer status record, such that an adjustedresource offer set may further be updated.

At step 1034, the resource offer generation system 1003 generates aresource offer set using a resource offer generation model. In someembodiments, the resource offer generation model may be embodied by oneor more algorithms for generating one or more resource offer dataobjects having particular resource offer values. In other embodiments,the resource offer generation model may be an algorithmic modelconfigured to generate the resource offer set based on one or more inputparameter sets. For example, the resource offer generation model may bebased on the resource offer generation input data sets. In otherembodiments, the resource offer generation model may be a machinelearning model configured to predict a resource offer set.

The resource offer generation model may be configured, and/or trained,to generate the resource offer set based on the one or more of theresource offer generation input data sets. The resource offer generationinput data sets may, additionally or alternatively, include one or moredata sets received from, or output from, a prediction system, or otherdata sets derived from data sets received from or output from theprediction system. For example, the resource offer generation model maygenerate the resource offer set based on, at least in part, an expectedresource volume data set for the various channel profiles allocated bythe prediction system. In a particular example of used mobile deviceacquisition and distribution, the expected resource volume data set mayinclude an expected, or predicted, set of resources to be distributedassociated with the efficient channel allocations of resources (e.g., anumber of resources associated with various resource set identifiers).Additionally or alternatively, the resource offer generation model maygenerate the resource offer set based on, at least in part, an averagedistribution term data set. In the particular example of used mobiledevice acquisition and distribution, the average distribution term dataset may include an expected sales price at which resources are to bedistributed via the efficient channel allocations. The averagedistribution term data set may, for example, include pricecharacteristics for various resource set identifiers, and/or may bebased on, or include, a decay parameters data object associated with adecay curve to estimate changes in the expected price characteristic forthe various resource set identifiers due to an expected time intervalfor distribution. In some embodiments, the resource offer generationmodel may retrieve, receive, or otherwise obtain the decay parametersdata object for use in determining an expected price characteristic fordistribution of resources associated with one or more resource setidentifiers based on a distribution time delay input parameter includedin the benchmark and portfolio target data set. For example, the averagedistribution term data set may be adjusted based on the decay curve andthe distribution time delay input parameter. In some embodiments, theexpected resource volume data set and average distribution term dataset, a combination of these sets, or a portion or combination ofportions thereof, and/or the market intelligence data set and/orportions of the market intelligence data set, may be used to derive aprojected receipts data set for the distribution of expected resources,such as used mobile devices, through the efficient channel allocationsassociated with the conditions or characteristics, such as pricecharacteristics, predicted by the prediction system. In someembodiments, resource offer generation model may be configured togenerate the resource offer set based, at least in part, on theprojected receipts data set applied to the resource offer generationmodel.

The resource offer generation system 1003 may be configured to access adatabase, for example embodied by the resource offer generation systemdatabase 802C, to retrieve and/or utilize the one or more resource offergeneration input data sets. In some embodiments, the database may beupdated, at least in part, by the prediction system. Alternatively, insome embodiments, the resource offer generation system 1003 may beconfigured to retrieve at least a portion of the resource generationinput data sets from the prediction system and/or an associateddatabase, such as the prediction system database 102C.

The resource offer generation model may be configured to generate theresource offer set where the resource offer set such that the resourceoffer set includes resource offer data objects for various resource setidentifiers that satisfy a desired benchmark and portfolio target dataset. The benchmark and portfolio target data set may include some or allof the data collection parameters input by an offer control user at anearlier step. For example, in some embodiments, a particularregion-program data object having the input region-program identifiermay be associated with one or more particular data collection parametersthat define a benchmark and portfolio target data set (e.g., financialtarget parameters that a resource offer set must satisfy). Additionally,in some embodiments, one or more of the data collection parameters maybe associated with a default parameter value, which may be altered viainput by the offer control user, for example provided at step 1024.

In some embodiments, the resource offer generation model may utilize, orotherwise be associated with, one or more sub-models for generating theresource offer set. For example, the resource offer generation model maycomprise at least an offer optimization model configured for optimizinga resource offer set, or generating an optimized resource offer set,based on one or more boundary conditions, such as a benchmark andportfolio target data set, and/or other applied data sets. Additionallyor alternatively, for example, the resource offer generation model maybe associated with, or utilize, an exception detection model configuredto generate a fair market offer set for various resources, such as thoseto be acquired based on a resource list data set and/or expectedresource volume data set. In some embodiments, the market intelligencedata set, and/or one or more subsets thereof, may be utilized along withthe fair market offer set generated by the exception detection model togenerate the resource offer set. For example, the market intelligencedata set may include a maximum third-party offer data object setcomprising a maximum third-party offer data object associated with eachresource set identifier for one or more third-party entities, and/or anaverage third-party offer data object set comprising an averagethird-party offer data object associated with each resource setidentifier for one or more third-party entities. Each averagethird-party offer data object may comprise and/or otherwise represent anaverage price characteristic, associated with a particular third-partyentity, for the resource set identifier

In some embodiments, one or more data sets are applied to the resourceoffer generation model to generate the resource offer set. For example,in some embodiments, the maximum third-party offer data object set, theaverage third-party offer data object set, the fair market offer set,the benchmark and portfolio target data set, and/or one or more resourceoffer generation input data sets. The various applied data sets may beapplied such that he resource offer generation model may generate theresource offer set comprising one or more resource offer data objectsassociated with a price characteristic, such as a resource offer value,such that the resource offer set satisfies the benchmark and portfoliotarget data set.

The resource offer generation model may generate an optimal resourceoffer set based on the various applied data sets. For example, theresource offer set may be generated to optimally satisfy the benchmarkand portfolio target data set. In this regard, the resource generationmodel may be configured to utilize one or more algorithms, statisticalmodels, and/or machine learning models for generating the resource offerset. In some embodiments, the resource offer generation model mayutilize an expected resource volume data set and/or an averagedistribution term data set to generate a resource offer set. Forexample, in some embodiments, the resource offer generation modelcomprises at least a linear optimization model configured to, based onthe various input data sets, generate the resource offer set tooptimally satisfy the benchmark and portfolio target data set. Forexample, based on the expected channel profile allocations and pricecharacteristic for the resource set identifiers to be distributed (e.g.,as identified in an input expected resource volume data set and anaverage distribution term data set), the resource offer generation modelmay generate the resource offer set to maximize satisfaction of thebenchmark and portfolio target data set. For example, in someembodiments, the benchmark and portfolio target data set may onlyinclude a minimum resource margin for resource offer data objects in thegenerated resource offer set. In other embodiments, the benchmark andportfolio target data set may include boundary conditions for theacquisition and distribution of specific resources, for example bymaximizing the price characteristic of resource offer data objectsassociated with a subset of resources (e.g., resources associated with aparticular resource set identifier). It should be appreciated that thebenchmark and portfolio target data set may serve as any number ofboundary conditions and any type of boundary conditions for optimizingthe resource offer set, for example a minimum profit for the resourceoffer data set, a desired margin per resource, a minimum profit based onmaximizing resource offer data objects associated with a particularsubset of resources (e.g., resources associated with a promotion),distribution channel profile mixes, and/or other financial analysistargets.

In some embodiments, some or all of the resource offer generation modelis maintained and performed via a sub-server and/or second servermanaged by, and/or otherwise associated with the resource offergeneration system 1003. For example, a second server communicable and/orcontrolled by the resource offer generation system 1003 may bemaintained to generate the resource offer set and/or optimize theresource offer set. The server managing the resource offer generationmodel may be configured for using to implement the model using anynumber of a myriad of programming implementations. For example, in someembodiments, the resource offer generation model may be configured usingthe R programming language, where the second or sub-server is configuredwith an environment for interfacing with the model.

The resource offer generation system 1003 may initiate the resourceoffer generation model, or one or more operations performed by theresource offer generation model, on the second or sub-server via one ormore APIs and/or services for communicating with the second orsub-server. In some embodiments, the resource offer generation system1003 may transmit one or more requests to initiate and/or apply one ormore of the input data sets to the resource offer generation model onthe second or sub-server. For example, in some embodiments, the resourceoffer generation system 1003 manages a database environment, such as aSQL environment for managing various data warehouse modules comprisingthe input data sets, and uses one or more SQL server integrationservices (SSIS) for pushing resource offer generation input data setsand/or generated data sets to the second or sub-server for applying tothe resource offer generation model. Upon output of the resource offerby the resource offer generation model, the generated resource offer set(or corresponding data) may be pushed back from the second orsub-server, for example to the resource offer generation system 1003,for storage in the database environment (e.g., such as an SQLenvironment) using one or more APIs and/or services, such as the one ormore SSIS associated with the SQL environment.

In some embodiments, the resource offer generation system 1003 directlycontrols and/or accesses the resource offer generation model to generatethe resource offer set. For example, the resource offer generationsystem 1003 may perform all operations described with respect to theabove steps on the same system (e.g., server or group of servers) asopposed to an separate system.

In some embodiments, the generated resource offer set is storedassociated with an offer status record for the region-program identifierand collection period data object. Additionally or alternatively, theoffer status indicator included in, or associated with, the offer statusrecord may be updated to embody or represent a pending adjustment statusindicator. For example, the offer status record may be retrieved, froman offer approval repository maintained or otherwise accessible to theresource offer generation system 1003 by querying the offer approvalrepository based on the region-program identifier and collection perioddata object, and receiving the offer status record as result data.

At step 1036, the resource offer generation system 1003 notifies theoffer control user that the resource offer generation model hascompleted generation and/or optimization of the resource offer set, andpushed the generated resource offer set to the database for retrieval.In some embodiments, a notification may be transmitted associated withthe user account of the offer control user utilized to access theresource offer generation system 1003 and perform the resource offergeneration process. In some embodiments, the offer control user may benotified via an application, interface, or other service associated withthe resource offer generation system 1003. In other embodiments, theoffer control user may be notified via a third-party application,interface, or other services, such as via an email transmitted to anemail account associated with the offer control user (e.g., an emailassociated with the user account associated with the offer controluser).

FIG. 11 illustrates an example data flow diagram 1100 for rendering aresource offer set, adjusting the resource offer set, submitting theadjusted resource offer set for approval, and approving or rejecting theadjusted resource offer set. These operations are performed via aplurality of specific interfaces corresponding to, and configured for,enabling such operations. The data flow diagram 1100 includes data flowsteps between components, such as of an sub-systems of the system 800,including client device 1001, resource offer generation system 1003, andapproval device 1005. The data flow diagram 1100 may be performed aftersome or all of the steps described with respect to data flow diagram1000 above.

In data flow 1100, several steps illustrated may be optional. Optionalsteps are illustrated in FIGS. 10 and 11 in broken lines. In someembodiments, one or more of the optional steps may be performed. In someembodiments, all optional steps may be performed.

At step 1102, the offer control user may access the resource offergeneration system, such as resource offer generation system 1003. Theoffer control user may access the resource offer generation system 1003the client device 1001, or another of a plurality of client devices.Upon re-accessing the resource offer generation system 1003, the offercontrol user may re-authenticate and/or otherwise begin a newauthenticated session, or continue an existing authenticated session.

At step 1104, the resource offer generation system 1103 generates,and/or transmits, a control signal causing a renderable objectcomprising an offer adjustment interface displayed at one or more clientdevices, such as the client device 1001. In some embodiments, thecontrol signal(s) may be transmitted to a second client device accessedby the offer control user associated with the client device 1001 (e.g.,a second computer or mobile device with which the offer control useraccessed the resource offer generation system 1003 and began anauthenticated session). The offer adjustment interface comprises anindication of the resource offer set. For example, the offer adjustmentinterface may comprise the resource offer value for one or more resourceoffer data objects in the resource offer set (e.g., a portion of theresource offer set may be visible). In some embodiments, the resourceoffer set is retrieved from a storage or database. For example, theresource offer set may be the generated and/or optimized resource offerset from an earlier step, which was stored associated with theregion-program identifier.

The offer adjustment interface may be configured to enable adjustment ofresource offer set. For example, the offer adjustment interface may beconfigured to enable the offer control user to adjust the resource offervalue associated with each resource offer data object in the resourceoffer set. In some embodiments, the offer control user may select aresource offer data object for adjusting, and input, via user engagementfor example, an adjusted resource offer value for said selected resourceoffer data object. After adjusting a resource offer data object, theoffer control user may continue to adjust other resource offer dataobjects, or adjust the same resource offer data object again. The offeradjustment interface may be dynamically rendered to reflect updatesbased on the adjustments performed by the offer control user.

The control signal causes rendering, to at least the client device 1001,of the offer adjustment interface including at least the indication ofthe resource offer set. In some embodiments, the offer adjustmentinterface further comprises an indication of data from the resourceoffer generation input data sets, or data derived therefrom. Forexample, in some embodiments, the offer adjustment interface comprisesmarket intelligence data, or representations of the market intelligencedata, for each resource offer data object in the resource offer set. Forexample, the market intelligence data rendered to the offer adjustmentinterface may include one or more third-party offers, such as competitoroffers. The offer adjustment interface may further comprise anindication of an offer analytics data set associated with the resourceoffer set. For example, the offer analytics data set may includeexpected profit-per-resource, resource margin information, and/orsummary information regarding the resource offer set (e.g., number ofresources associated with a resource offer data object currentlyassociated with a resource offer value). In some embodiments, theresource offer generation system 1103 may calculate, or otherwisedetermine, the offer analytics data set based on the resource offer setand one or more resource offer generation input data sets, such as themarket intelligence data set. Alternatively, the resource offergeneration model and/or exception detection model may be configured togenerate the offer analytics data set associated with the resource offerset.

In some embodiments, the offer adjustment interface additionallycomprises a dashboard for accessing and/or rendering various separateanalysis interfaces for analyzing the resource offer set, and anyadjustments. One or more of the analysis interfaces may provideindications of data for analyzing the adjusted resource offer set withregard to one or more third-party offers. For example, the analysisinterfaces may provide data derived based on the currently adjustedresource offer data set and one or more portions of the resource offergeneration input data sets.

At step 1106, the client device 1001 renders the offer adjustmentinterface. The offer adjustment interface may be rendered such that theoffer control user can view the offer data objects associated withvarious resources. The offer adjustment interface may be configured toenable adjustment of each of the resource offer data objects in theresource offer set, for example in response to user engagement with theoffer adjustment interface to change the offer value.

At step 1108, client device 1001, and/or the offer control user via theclient device 1001, may analyze the rendered resource offer set. In someembodiments, the offer control user may view the resource offer valuesassociated with each offer data object in the resource offer set. Theoffer control user may, additionally or alternatively, analyze one ormore indications of an offer analytics data set rendered via the offeradjustment interface. For example, the price adjustment interface maycomprise a dashboard for accessing various analysis interfaces, such asthe interfaces illustrated by FIGS. 14 and 15, and an indication of anoffer analytics data set which may be analyzed to determine whether toadjust the resource offer values for one or more resource offer dataobjects in the resource offer set. In some embodiments, the clientdevice 1001 may be configured to analyze the resource offer setautomatically. For example, the client device 1001 may be configured toperform one or more analysis algorithms based on the resource offer setand/or offer analytics data set to determine whether one or more of theresource offer data objects should be adjusted.

At step 1110, client device 1001, and/or the offer control user via theclient device 1001, may adjust the resource offer set. In someembodiments, the offer control user may adjust the resource offer valuefor one or more resource offer data objects. For example, the offercontrol user may, via user engagement with the price adjustmentinterface, input an adjusted resource offer value for one or moreresource offer data objects. The adjustments to the resource offer setmay be performed based on the analysis of the information rendered tothe offer adjustment interface.

In some embodiments, the offer control user may, via the client device1001, save adjustments to the resource offer generation system 1003after adjusting at least one resource offer data object. For example,the offer adjustment interface may include an offer saving componentconfigured to, in response to user engagement, generate and/or transmitone or more control signals to the resource offer generation system1003, the control signal(s) comprising at least one adjustment dataobject for each adjusted resource offer data object. The resource offergeneration system 1003 may then update the stored resource offer setbased on the received adjustment data objects to create an new adjustedoffer set. In other embodiments, one or more control signal(s) aregenerated and/or transmitted automatically in response to input, by anoffer control user, for adjusting a resource offer data object, forexample in response to input of an adjusted resource offer value for aparticular resource offer data object.

In some embodiments, the stored resource offer set to be updated isretrieved associated with the region-program identifier and collectionperiod data object. For example, the stored resource offer set may beretrieved from an offer approval repository and associated with acorresponding offer status record, based on the region-programidentifier and collection period data object, from another repository orsub-repository. If no adjustments have been saved previously, the storedresource offer set may be the resource offer set generated by theresource offer generation model. Alternatively, if one or moreadjustments have been saved, the stored resource offer set may be anadjusted resource offer set created based on one or more previouslysaved adjustment data objects.

The created new adjusted resource offer set may then be stored, forexample associated with the region-program identifier and the collectionperiod data object, to replace the previously stored resource offer set.The new adjusted resource offer set may be retrieved and updated whensubsequent updates are performed by an offer control user.

In some embodiments, components of the offer adjustment interface may bedynamically updated in response to the adjustment(s). For example, theadjusted resource offer set may include one or more offer resource dataobjects associated with an adjusted offer value, which may bedynamically updated via the offer adjustment interface. Additionally, anoffer analytics data set may be recalculated or determined, and anindication of the offer analytics data set may be updated to render theupdated offer analytics data set to the interface. For example, one ormore offer analysis algorithms for determining, identifying, orotherwise calculating an offer analytics data set may be performed uponadjustment of one or more of the resource offer data objects, and anindication of the offer analytics data set rendered to the offeradjustment interface may be updated dynamically, in real-time, toreflect the output from said algorithm(s). In some embodiments, one ormore analysis interfaces accessible via a rendered dashboard may bedynamically updated upon adjustment of one or more of the resource offerdata objects. Dynamically updating rendering of the offer adjustmentinterface enables the offer control user to immediately visualize theeffects of adjusting one or more offer data object(s), and continue toadjust the resource offer set in real-time.

At step 1112, client device 1001 submits completion of the adjustedresource offer set. In some embodiments, the client device 1001generates and/or transmits a completion control signal to the resourceoffer generation system 1003 indicating that the adjusted resource offerset is finalized to submit for approval from an offer approval user. Inother embodiments, the completion control signal comprises one or moreadjustment data objects for updating the resource offer data set tocreate the adjusted resource offer set. In other embodiments, thecompletion control signal comprises the adjusted resource offer setitself, for example created by the user device 1001. The adjustedresource offer set may reflect all the adjustments made to resourceoffer data objects in the resource offer set. In some embodiments, theoffer adjustment interface additionally comprises an interfacecomponent, such as an offer submitting component, that the offer controluser may engage to cause generation and/or transmission of thecompletion control signal.

At step 1114, resource offer generation system 1003 may receive thecompletion control signal from the user device 1001. In someembodiments, in response to the completion control signal, the resourceoffer generation system 1003 may update and/or store the adjustedresource offer set. In some embodiments, the resource offer generationsystem 1003 retrieves an offer status record associated with theregion-program identifier and collection period data object, for examplefrom an offer approval repository, and updates an associated offerstatus indicator to represent a pending approval status indicator. Insome embodiments, the resource offer generation system 1003 creates theadjusted resource offer, for example by updating the previously storedresource offer set based on one or more adjustment data objects.Alternatively, in some embodiments, the resource offer set storedassociated with the region-program identifier and collection period dataobject, for example in an offer approval repository or anotherrepository, may updated based on an adjusted resource offer set parsedand/or extracted from the completion control signal.

At optional step 1116, resource offer generation module 1003 notifies anoffer approval user that the adjusted resource offer set has beensubmitted and stored. In some embodiments, a notification may betransmitted associated with the user account of the offer approval user,such that the offer approval user may access retrieve the notificationby accessing the resource offer generation system 1003. In someembodiments, the offer approval user may be notified via an application,interface, or other service associated with the resource offergeneration system 1003. In other embodiments, the offer approval usermay be notified via a third-party application, interface, or otherservices, such as via an email transmitted to an email accountassociated with the offer approval user (e.g., an email associated withthe user account of the offer approval user).

At step 1118, an offer approval user accesses the resource offergeneration system, such as resource offer generation system 1003. Theoffer approval user may access the resource generation system 1003 viaan approval device 1005. The approval device may be a second clientdevice in communication with the resource offer generation system 1003.For example, the second client device may be embodied by a secondrequest source system 104. The approval device 1005 may be configured toexecute an application, interface, web/browser application, or the likefor accessing the resource offer generation system 1003. Theapplication, interface, web/browser application, or the like, foraccessing the resource offer generation system 1003 as an offer approvaluser may be different from the application, interface, web/browserapplication, or the like, for accessing the resource offer generationsystem 1003 as an offer control user. Alternatively, the application,interface, web/browser application, or the like for accessing theresource offer generation system 1003 may be the same for offer approvalusers and offer control users. An offer control user may be associatedwith a user account that has permissions for creating and/or editingregion-program data objects, requesting resource offer generation,accessing an offer adjustment interface, and submitting adjustment offersets.

An offer approval user may be associated with a user account that haspermissions for accessing submitted adjusted resource offer sets,accessing corresponding offer adjustment interfaces, and responding tosubmitted adjusted resource offer sets (e.g., approving or rejectingsubmitted adjusted resource offer sets). For example, the resource offergeneration system 1003 may provide, to the admin device 1005, one ormore adjusted resource offer sets stored associated with one or moreoffer status records including, or associated with, a pending approvalstatus indicator, where each adjusted resource offer set and offerstatus record is associated with a particular region-program identifierand collection period data object. The offer approval user may thenselect an adjusted resource offer set for a particular region-programidentifier and collection period data object that the offer approvaluser would like to view, analyze, and/or approve or reject.

At step 1120, resource offer generation system 1003 may generate, and/ortransmit, an approval request control signal causing a second renderableobject comprising an approval interface displayed at another of one ormore client devices, such as the approval device 1003. The approvalrequest control signal may be generated and/or transmitted in responseto selection of an adjusted resource offer set for a particularregion-program identifier and collection period data object. Theapproval interface comprises an indication of the adjusted resourceoffer set. The adjusted resource offer set may be retrieved from astorage upon access by an offer approval user, for example via admindevice 1003. For example, the offer approval user may select to view anadjusted resource offer set submitted associated with a particularregion-program data object having a particular region-programidentifier.

In some embodiments, the approval interface comprises additionalindications of data. The approval interface may comprise the sameindications of data rendered to the offer adjustment interface providedto an offer control user via client device 1001. For example,additionally or alternatively, the approval interface may furthercomprise an indication of data from the resource offer generation inputdata sets, or data derived therefrom. The approval interface, in someembodiments, further comprises market intelligence data, orrepresentations of the market intelligence data, for each resource dataobject in the resource offer set. For example, the market intelligencedata rendered to the approval interface may include one or morethird-party offers, such as competitor offers. The approval interfacemay further comprise an indication of an offer analytics data setassociated with the adjusted resource offer set. For example, the offeranalytics data set may include expected profit-per-resource, resourcemargin information, summary information regarding the resource offerset, and/or the like. The offer analytics data set may be calculated, orotherwise determined, based on the submitted adjusted resource offer setand one or more resource offer generation input data sets, and thus notmodifiable by the offer approval user.

Additionally, the approval interface may include the same dashboardrendered to the offer adjustment interface. Accordingly, in suchembodiments, the approval interface enables the offer approval user toanalyze the submitted adjusted resource offer set based on the sameindications of data and visualizations used by the offer control user toperform adjustments on the adjusted resource offer set.

At step 1122, the admin device 1005, and/or the offer approval user viathe admin device 1005, may analyze the adjusted resource offer set. Insome embodiments, the offer approval user may view the resource offervalues associated with each resource offer data object in the adjustedresource offer set. The offer approval user may, additionally oralternatively, analyze an indication of an offer analytics data setrendered via the approval interface. For example, the approval interfacemay include an indication of an offer analytics data set (e.g., margin,resource profit, offer summary data, or other financial targetinformation), which may be analyzed to determine whether to accept orreject the adjusted resource offer set. Additionally or alternatively,the approval interface may comprise a dashboard, for accessing variousanalysis interfaces for analyzing the adjusted resource offer set, forexample in view of a benchmark and portfolio target data set. Forexample, the analysis interfaces may include one or more interfaces forvisualizing offer strength for the adjusted resource offer set, pricetrends associated with the adjusted resource offer set, marketcomparison associated with the adjusted resource offer set, and thelike.

The offer approval user may, via the various interfaces and indicationstherein, analyze the adjusted resource offer set based on an identified,received, or offline benchmark and portfolio target data set. Thebenchmark and portfolio target data set may include one or moreprofitability, margin, or other financial targets for the region-programidentifier associated with the adjusted resource offer set. In someembodiments, the approval device 1005 may be configured to analyze theadjusted resource offer set automatically. For example, the approvaldevice 1005 may be configured to perform one or more offer approvalalgorithms based on the adjusted resource offer set and/or offeranalytics data set to determine whether the adjusted resource offer setshould be approved or rejected.

At step 1124, the offer approval user may, via the admin device 1005,engage the approval interface to approve or reject the adjusted resourceoffer set. For example, in some embodiments, the offer approval user mayengage a first interface component for approving the resource offer set,or a second interface component for rejecting the resource offer set.The resource offer set may be approved or rejected based on the analysisperformed at step 1122.

At step 1126, the admin device 1005 may determine whether the offerapproval user approved the adjusted resource offer set or rejected theadjusted resource offer set. In some embodiments, the determinationdepends on the user interface component engaged by the offer approvaluser at step 1124. In other embodiments, an offer approval controlsignal is transmitted to the resource offer generation system 1003 at orafter step 1124, and the determination is based on a control signalreceived from the resource offer generation system 1003 in response tothe offer approval control signal.

Flow may continue to optional step 1128 in a circumstance where theoffer approval user rejected the adjusted resource offer set. Atoptional step 1128, the admin device 1005, and/or the offer approvaluser via the admin device 1005, may create an offer rejection messageassociated with the adjusted resource offer set. In some embodiments, auser interface, or a user interface component, is rendered to enable theoffer approval user to input and submit an offer message to create it.In some embodiments, the interface may provide one or more predeterminedoffer rejection messages, and/or an free-text input to enable input of acustom offer rejection message. The offer rejection message may becreated after the offer approval user rejects, or indicates a desire toreject, the resource offer set, for example by engaging a user interfacecomponent for rejecting the resource offer set. The offer rejectionmessage may reflect the analysis of the adjusted resource offer setand/or determination of the adjusted resource offer set, an explanationdefining why the adjusted resource offer set is rejected, and/oradjustment steps to be performed to improve the adjusted resource offerset for approval. Alternatively, in some embodiments, the offerrejection message may be created, and/or created and submitted by anoffer approval user before rejecting the adjusted resource offer set. Insome embodiments, a user interface component is provided for submittingthe rejection of the adjusted resource offer set and the offer rejectionmessage.

The admin device 1005 may transmit an offer approval response to theresource offer generation system 1003 in response to submission of theapproval or rejection. For example, an offer approval response may begenerated and/or transmitted in response to the engagement with theapproval interface to approve or reject the adjusted resource offer set,or in some embodiments in response to engagement with a user interfacecomponent for submitting the offer rejection message. The offer approvalresponse may include at least an offer approval status indicating theapproval or rejection of the adjusted resource offer set. In someembodiments, if the offer approval status is a rejection status (e.g.,the offer approval user rejects the adjusted resource offer set), theoffer approval response may additionally include the created offerrejection message, if the offer approval user created one.

At step 1130, the resource offer generation system 1003 may receive anoffer approval control signal comprising at least an offer statusindicator, where the offer status indicator is represented or otherwiseembodied by a rejection status indicator. Additionally or alternatively,in some embodiments, the offer approval control signal may include theoffer rejection message created by the offer approval user. Uponreceiving the offer approval control signal, the resource offergeneration system 1003 may parse the control signal to identify theoffer status indicator.

The resource offer generation system 1003 may additionally store theadjusted resource offer set associated with the region-programidentifier, collection period data object, and the offer statusindicator (e.g., the rejected status indicator). In some embodiments,the resource offer generation system 1003 may update a correspondingoffer status record in an offer approval repository, sub-repository, ortable. For example, the resource offer generation system 1003 may updatean offer status record associated with the adjusted resource offer setto include the rejection status indicator. For example, the resourceoffer generation system 1003 may retrieve an offer status record from arepository, such as an offer approval repository, based on theregion-program identifier and collection period data object. The offerstatus indicator associated with, or included in, the offer statusrecord may be updated based on the received and/or identified offerstatus indicator, for example to represent the rejected statusindicator.

At step 1132, the resource offer generation system 1003 may provide arejection notice to the offer control user. The resource offergeneration system 1003 may generate, retrieve, and/or otherwiseconfigure the rejection notice. The rejection notice may comprise theoffer rejection message received from the offer approval user via theapproval device 1005. The rejection notice may be stored associated withthe user account for the offer control user, such that the offer controluser may access the rejection notice upon subsequent access of theresource generation system via the user account.

The resource offer generation system 1103 may generate and/or configureone or more control signals for causing rendering of the rejectionnotice to the client device 1001. The control signal(s) may be generatedor configured to include a renderable data object associated with, orincluding, the rejection notice. The control signal(s) may betransmitted to the client device 1001 to cause rendering of aninterface, or an interface component, including the rejection notice. Insome embodiments, the control signal(s) are transmitted after subsequentaccess of the resource offer generation system 1003 by an offer controluser, such as via client device 1001 or another client device.

At step 1134, the offer control user may access the resource offergeneration system, such as resource offer generation system 1003. Theoffer control user may access the resource offer generation system 1003via a client device, such as client device 1001. The offer control usermay again access the resource offer generation system 1003 via theclient device 1001 after a period of time since submitting the adjustedresource offer set for approval. In some embodiments, the offer controluser may not have ended an authenticated session associated withaccessing the resource offer generation system since beginning theresource offer generation process or submitting the adjusted resourceoffer set for approval, and thus may re-access the resource offergeneration system 1003 without subsequent authentication. In otherembodiments, the offer control user may re-authenticate themselves viathe client device 1001 to begin another authenticated session foraccessing the resource offer generation system 1003.

At step 1136, the client device 1001 may render the rejection notice. Insome embodiments, the rejection notice may be rendered to an interfaceassociated with the region-program data identifier and the collectionperiod data object, for example rendered to an interface where the offercontrol user may view each region-program data object and/or associatedinformation, each collection period data object for which a resourceoffer generation has been imitated, an associated offer statusindicator, and/or, when available, the rejection notice for one or morerejected adjusted resource offer sets for a particular region-programidentifier and collection period data object (e.g., based on offerstatus records including, or associated with, a rejected statusindicator.

The offer control user may then access the rejected adjusted resourceoffer set to make further adjustments to resubmit a newly adjustedresource offer set for approval. Flow may then return to step 1106,where an offer adjustment interface is rendered to the client device1001 for accessing by the offer control user via the client device. Theoffer control user may engage the offer adjustment interface to adjustthe adjusted resource offer set and resubmit for approval. In someembodiments, the cycle defined by steps 1106-1136 may be repeated once,twice, or more times until the adjusted approval by an offer approvaluser.

Returning to step 1126, flow may continue to step 1138 in a circumstancewhere the offer approval user rejected the adjusted resource offer set.At step 1138, the resource offer generation system 1003 may receive anoffer approval control signal comprising at least an offer statusindicator, where the offer status indicator is represented or otherwiseembodied by an approved status indicator. Upon receiving the offerapproval control signal, the resource offer generation system 1003 mayparse the control signal to identify the offer status indicator.

The resource offer generation system 1003 may additionally store theadjusted resource offer set associated with the region-programidentifier, collection period data object, and the offer statusindicator (e.g., the approved status indicator). In some embodiments,the resource offer generation system 1003 may update a correspondingoffer status record in an offer approval repository, sub-repository, ortable. For example, the resource offer generation system 1003 may updatean offer status record associated with the adjusted resource offer setto include the approved status indicator. For example, the resourceoffer generation system 1003 may retrieve an offer status record from arepository, such as an offer approval repository, based on theregion-program identifier and collection period data object. The offerstatus indicator associated with, or included in, the offer statusrecord may be updated based on the received and/or identified offerstatus indicator, for example to represent the approved statusindicator.

At step 1140, upon updating the offer status record based on theapproved status indicator, the resource offer generation system 1003 maygenerate and provide an approval notice to one or more users of theresource offer generation system 1003. In some embodiments, for example,the region-program identifier associated with the approved adjustedresource offer set may be similarly associated with one or more useraccounts, such as the user account for the offer control user thatsubmitted the adjusted resource offer set, a user account associatedwith an executive leader for the region-program data object, and/or oneor more user accounts associated with sales or distribution users forthe region-program data object. In some embodiments, the approval noticeincludes an indication of the approved status indicator and/or theadjusted resource offer set as approved.

The approval notice may be generated and/or transmitted in a myriad ofways. In some embodiments, the approval notice may be embodied by amessage stored by the resource offer generation system 1003 andaccessible via a client device during an authenticated session (e.g.,via a messenger or notification system accessible via the resource offergeneration system 1003). In other embodiments, the approval notice maybe an email data object generated and/or transmitted by the resourceoffer generations system 1003 to one or more associated email servicesassociated with one or more email recipients of the approval notice.

After step 1140 is completed, the adjusted resource offer set may bedistributed, for example by the resource offer generation system 1003and/or one or more of the notified users, to one or more entities, suchas one or more third-party entities associated with various resourceacquisition and/or distribution channels within the region associatedwith the region component of the region-program identifier. The adjustedresource offer set may then be utilized for resource acquisition withinthat region, for example by offering to acquire resources associatedwith a particular resource set identifier at a predefined pricecharacteristic defined by the resource offer value for the resourceoffer data object in the adjusted resource offer set associated with theparticular resource set identifier.

FIG. 12A is a flow chart of an example process 1200 for generating aresource offer set, adjusting the resource offer set, and receiving anoffer approval status for the adjusted resource offer set, in accordancewith some embodiments of the present disclosure. The operationsillustrated with respect to example process 1200 may be performed by aresource offer generation system, for example embodied by the apparatus900.

At optional block 1202, the apparatus 900 includes means, such as modelperformance circuitry 912, input/output circuitry 906, communicationscircuitry 908, processor 902, and/or the like, or a combination thereof,for receiving a region-program identifier and collection period dataobject. The region-program identifier may be received from a clientdevice to initiate resource offer generation associated with theregion-program data object having the region-program identifier. Thecollection period data object may be received from the client device,and comprise a collection period start date timestamp and a collectionperiod end date timestamp.

At optional block 1204, the apparatus 900 includes means, such as datamanagement circuitry 910, model performance circuitry 912, processor902, and/or the like, or a combination thereof, for determining theregion-program identifier and collection period data object are notassociated with a pending resource offer generation process. In someembodiments, the apparatus may query a repository, such as an offerapproval repository embodied by resource offer generation systemdatabase 802C, based on the region-program identifier and collectionperiod data object. If an offer status record is retrieved, for exampleas response data to the query, a resource offer generation process hasbeen initiated and/or completed for the region-program identifier andcorresponding collection period. If a record is retrieved, a secondresource offer generation process should not be initiated, and the flowmay terminate.

At block 1206, the apparatus 900 includes means, such as data managementcircuitry 910, communications circuitry 908, processor 902, and/or thelike, or a combination thereof, for retrieving at least one resourceoffer generation input data sets. In some embodiments the resource offergeneration input data sets may be retrieved from a repository, forexample by retrieving a base table linked to a plurality of data tablesrepresenting each resource offer generation input data set in aparticular database. In some embodiments, the resource offer generationinput data sets include a historical offer data set, a resource listdata set, a market intelligence data set, a resource mapping data set,an expected resource volume data set, an average distribution term dataset, and/or a projected receipts data set. The expected resource volumedata set may comprise, or otherwise be derived from, a subset of apredicted channel and condition data set output by a prediction system.For example, the expected resource volume data set may comprise thepredicted volume condition data from a predicted channel and conditiondata set output by the prediction system for various channel profiles.The average distribution term data set may comprise, or otherwise bederived from, a subset of the predicted channel and condition data setoutput by the prediction system. For example, the average distributionterm data set may comprise the predicted pricing characteristiccondition data from a predicted channel and condition data set output bythe prediction system. In some embodiments, combinations of thesevarious data sets are retrieved from one or more repositories and/ordatabases accessible by the apparatus 900 directly or accessible viacommunications with one or more other systems (e.g., throughcommunication with a prediction system).

In some embodiments, in generating a resource offer set, the resourceoffer generation model may utilize a trusted resource characteristicdata set generated by an exception detection model. In one such example,the trusted resource characteristic data set may include acharacteristic associated with the acquisition and/or distribution ofresources, for example the acquisition and distribution of used mobilephones. A non-limiting example may include generating trusted pricingcharacteristics for resource set identifiers, in a fair market offerset, based on one or more untrusted third-party resource pricing datasets and one or more distributed resource pricing data set(s). In thisregard, at block 1212, the apparatus 900 includes means, such as modelperformance circuitry 912, processor 902, and/or the like, or acombination thereof, for generating a fair market offer set using anexception detection model. It should be appreciated that, in someembodiments, the fair market offer set may not be generated.

Using acquisition of used mobile devices as an example, the one or moreuntrusted third-party resource pricing data sets and distributedresource pricing data sets may be applied to the exception detectionmodel to generated a trusted resource characteristic data set, forexample the fair market offer set. The fair market offer set may includea fair market offer data object for various resource set identifiers,each fair market offer data object having a pricing characteristic(e.g., a fair market offer value) for each of the resource setidentifiers. For example, each third-party resource pricing data set maybe associated with a particular third-party entity, for example acompetitor entity, and include records for an average pricecharacteristic for each resource set identifier offered by thethird-party entity set over a particular time interval (e.g., a weeklypricing value at which the third-party entity will purchase and/ordistribute the resource set identifier). The distributed resourcepricing data set may include an average offer value for each resourceset identifier offered by a user via a distributed user platform (e.g.,a weekly value at which the resource associated with the resource setidentifier can be purchased from an individual user via the distributeduser platform). The exception detection model may generate the trustedresource characteristic data set embodied by the fair market offer setvia the process described below with respect to FIG. 12B.

At block 1214, the apparatus 900 includes means, such as data managementcircuitry 910, model performance circuitry 912, communications circuitry908, processor 902, and/or the like, or a combination thereof, forreceiving a benchmark and portfolio target data set. The benchmark andportfolio target data set may be received from a client device, forexample in response to user input and submission by an offer controluser, or received from an approval device, for example in response toinput a submission by an offer approval user. Alternatively, in someembodiments, the benchmark and portfolio target data set may beretrieved from a database, such as resource offer generation systemdatabase 802C.

In some embodiments, the benchmark and portfolio target data set mayinclude one or more data collection parameter values for various datacollection parameters. In some embodiments, additionally oralternatively, the benchmark and portfolio target data set includesdefault values associated with the region-program data object having theregion-program identifier input at an earlier block. The benchmark andportfolio target data set may, for example, include various data objectsrepresenting boundary conditions for use in generating the resourceoffer set. For example, in one example context of used mobile deviceacquisition and distribution, the benchmark and portfolio target dataset may include portfolio level financial targets such that the resourceoffer set is generated such that the resource offer values for thevarious resource offer data objects satisfy the boundary conditionsrepresented by the benchmark and portfolio target data set.

At block 1216, the apparatus 900 includes means, such as data managementcircuitry 910, model performance circuitry 912, processor 902, and/orthe like, or a combination thereof, for generating a resource offer setusing a resource offer generation model. In some embodiments, theresource offer set is generated by applying at least one or more of theresource offer generation input data sets to the resource offergeneration model. Additionally or alternatively, the benchmark andportfolio target data set may be applied to the resource offergeneration model, for example such that the generated resource offer setmust satisfy the applied benchmark and portfolio target data set. Theresource offer set may include a resource offer data object associatedwith one or more resources to be acquired associated with theregion-program data object. The resource offer value for said resourceoffer data objects may represent a price at which a particular resourceis to be offered for acquisition from a resource owner through one ormore device acquisition channel profiles. In some embodiments, theresource offer generation model comprises an algorithmic modelconfigured to use the applied data sets to generate an output. In otherembodiments, the resource offer generation model comprises one or moreconfigured and trained machine learning models to use the applied datasets to generate an output.

In some embodiments, the resource offer generation model may compriseone or more algorithms and/or machine learning models for optimizingresource offer data objects for various resource set identifiers togenerate the optimal resource offer set to satisfy the benchmark andportfolio target data set. The benchmark and portfolio target data setmay serve as boundary conditions for optimizing the generated resourceoffer set. For example, in some embodiments, the benchmark and portfoliotarget data set may include a minimum profit, margin or profit perresource, and/or other financial analysis targets. The resource offergeneration model may comprise a linear optimization model configured tomaximize the resource offer set according to the benchmark and portfoliotarget data set. In some embodiments, the linear optimization model maybe embodied by, or configured to execute, on a second apparatus, system,or server. Accordingly, the apparatus 900 may include means to transmitan optimization request to the server, for example via one or more APIs,and receive the optimized resource offer set in response.

Using acquisition of used mobile devices as another example, theresource offer set may comprise resource data objects having pricecharacteristics for various resource set identifiers generated tooptimally satisfy an applied benchmark and portfolio target data set.The resource offer generation model may, for example, generate deviceoffer values for various user mobile devices associated with variousresource set identifiers, for example a CNN, such that the overalldevice offer set for all devices satisfies the user input values and/ordefault values for the parameters associated with the benchmark andportfolio target data set (e.g., a desired profit margin, channelprofile mix, device resource set identifiers or CNNs offered aspromotions, and the like). The resource offer generation model mayconsider the efficient resource allocation to one or more channelprofiles and/or corresponding predicted price characteristic associatedwith the distribution of a particular resource set identifier, forexample generated by a prediction system, to generate optimal resourceoffer data objects for the various resource set identifiers.

The resource offer generation model may utilize one or more other datasets applied, for example one or more other resource offer generationinput data sets (such as an offer history data set and/or marketintelligence data set) and/or one or more data sets derived by anexception detection model, to identify price characteristic targets toattempt to exceed in generating the resource offer data objects includedin the resource offer set while satisfying boundary conditionsrepresented by the benchmark and portfolio target data set. For example,in some embodiments, the resource offer generation model may perform oneor more algorithms, machine learning models, or the like, to firstattempt to generate the resource offer set to include resource offerdata objects associated with a price characteristic (e.g., a resourceoffer value) that satisfies, such as by exceeding, a maximum pricecharacteristic for each resource set identifier, or one or morepromotional resource set identifier. If the resource offer generationmodel determines the resource offer set cannot be generated to satisfythe maximum price characteristic for each resource set identifier, orone or more promotional resource set identifiers, the resource offergeneration model may second attempt to generate the resource offer setto include resource offer data objects associated with a pricecharacteristic that satisfies, such as by exceeding, an average pricecharacteristic for each resource set identifier, or the one or morepromotional resource set identifier. If the resource offer generationmodel determines the resource offer set cannot be generated to satisfythe average price characteristic for each resource set identifier, orthe one or more promotional resource set identifier, the resource offergeneration model may third attempt to generate the resource offer set toinclude resource offer data objects associated with a pricecharacteristic that satisfies, such as by exceeding, a pricecharacteristic for the resource set identifier associated with the fairmarket offer set.

At block 1218, the apparatus 900 includes means, such as data managementcircuitry 910, model performance circuitry 912, processor 902, and/orthe like, or a combination thereof, for generating a control signalcausing a renderable object comprising an offer adjustment interfacedisplayed at a first of one or more client devices. The client devicemay be a particular client device associated with an offer control userauthenticated with the apparatus 900 for an authenticated session. Thecontrol signal may cause rendering of the offer adjustment interface.

The offer adjustment interface may include each resource offer value foreach resource offer data object in the resource offer set. The offeradjustment interface may, additionally, include an indication of anoffer analytics data set, such as portfolio level financial values basedon the adjusted resource offer set and market intelligence data. Furtherin some embodiments, the offer adjustment interface comprises dashboardfor accessing one or more analysis interfaces, each analysis informationcomprising one or more indication(s) of data based on, or derived from,the resource offer set and/or various portions of market intelligencedata. The apparatus may cause rendering of the offer adjustmentinterface by transmitting a renderable data object embodying the offeradjustment interface.

At block 1220, the apparatus 900 includes means, such as data managementcircuitry 910, communications circuitry 908, processor 902, and/or thelike, or a combination thereof, for updating the resource offer set toan adjusted resource offer set. The adjusted resource offer set mayinclude one or more resource offer data objects having adjusted offervalues input by the offer control user.

In some embodiments, the apparatus 900 may receive one or more controlsignals from one or more client devices, the control signals includingone or more adjustment data objects for use in updating the resourceoffer set. The resource offer set may be updated based on the one ormore adjustment data objects to create the adjusted resource offer set.For example, the one or more adjustment data objects may embody,represent, or otherwise include one or more adjusted resource offervalues for one or more resource offer data objects in the resource offerset.

In other embodiments, the adjusted resource offer set may be receivedfrom a client device. For example, a client device may update theresource offer set to create the adjusted resource offer set based onone or more adjustment data objects, where the apparatus 900 may receivethe adjusted resource offer set from the client device after saving,and/or saving and submission, of the adjusted resource offer set by anoffer control user via the client device.

At block 1222, the apparatus 900 includes means, such as communicationscircuitry 908, input/output circuitry 906, processor 902, and/or thelike, for generating a control signal, or multiple control signals,causing a renderable object comprising an approval interface displayedat a second of the one or more client devices. The second of the one ormore client devices may be an approval device associated with an offerapproval user authenticated with the apparatus 900 for an authenticatedsession. The control signal(s) may be transmitted, for example over anetwork, to cause rendering of the approval interface.

The approval interface may include the adjusted resource offer setsubmitted received from the client device associated with the offercontrol user, and/or additional information (such as a dashboard) foranalyzing the adjusted resource offer set. For example, the approvalinterface may additionally include an indication of an offer analyticsdata set calculated and/or otherwise determined based on the adjustedresource offer set. Additionally or alternatively, in some embodiments,the approval interface may include a dashboard for accessing one or moreanalysis interfaces based on the adjusted resource offer set. In someembodiments, the dashboard and indication(s) of the offer analytics dataset of the approval interface may comprise the same elements rendered tothe offer adjustment interface. The apparatus may cause rendering to anapproval device upon access of the apparatus by an offer approval uservia the approval device.

At block 1224, the apparatus 900 includes means, such as communicationscircuitry 908, processor 902, and/or the like, or a combination thereof,for receiving, from the approval device, an offer approval controlsignal comprising an offer status indicator. The offer approval controlsignal may be received from the approval device in response to userengagement with the approval interface. The offer status indicator mayrepresent an approved status indicator (for example, when the offerapproval user analyzes and/or approves the adjusted resource offer set)or a rejection status indicator (for example, when the offer approvaluser analyzes and/or rejects the adjusted resource offer set). The offerstatus indicator may be received in response to user engagement with theapproval interface, for example in response to user engagement with anoffer approval component or an offer rejection component of the approvalinterface.

At block 1226, the apparatus 900 includes means, such as data managementcircuitry 910, communications circuitry 908, processor 902, and/or thelike, or a combination thereof, for storing the adjusted resource offerset associated with the region-program identifier, the collection perioddata object, and the offer status indicator. In some embodiments, theapparatus may store the adjusted resource offer set and/or the offerapproval status associated with the adjusted resource offer set suchthat each is retrievable using the region-program identifier andcollection period data object. The apparatus may store the adjustedresource offer set and/or offer approval status in a database, forexample embodied by the resource offer generation system database 802C.

If the offer approval status is an approved status, the flow may end. Ifthe offer approval status is a rejection status, the flow may return toblock 1218 for adjustment by the offer control user via the clientdevice. This cycle may continue until the offer control user an approvedstatus is received for an adjusted resource offer set. The acceptedresource offer set may be used to provide one or more offers to variousthird-party entities for purchase of such resources.

FIG. 12B illustrates a flow chart of an example process 1200B forgenerating a trusted resource characteristic data set based on applyingone or more untrusted third-party resource characteristic data sets, andone or more characteristic data objects from a distributed userplatform, to an exception detection model in accordance with someembodiments of the present disclosure. The operations illustrated withrespect to example process 1200 may be performed by a resource offergeneration system, for example embodied by the apparatus 900.

One non-limiting example use case for generating a trusted resourcecharacteristic data set based on one or more untrusted third-partyresource data sets and a distributed resource characteristic data set isfor generating a fair market offer set for the acquisition of usedmobile devices. Each untrusted third-party resource characteristic dataset may include price characteristics for various used mobile devicesassociated with various resource set identifiers, where each untrustedthird-party resource characteristic data set is associated with beassociated with a different third-party entity. The untrustedthird-party resource characteristic data set may include historicalprices at which the third-party entity will purchase used mobile devicesfor various resource set identifiers. However, the untrusted third-partyresource characteristic data set is not trustworthy as a fair pricecharacteristic for each resource, as the price characteristic may beassociated with an exception period (e.g., where third-party entity mayoffer a promotion such that prices for particular used mobile devicesare elevated despite decreasing value of the device).

In this regard, a distributed resource characteristic data set is notaffected by promotions because the price characteristics are for offersfor resource acquisition and/or distribution by individual users of adistributed user platform. Unlike third-party entities that arecommercial resellers, individuals do not apply exception periods (suchas promotional periods for certain resources) to pricing characteristicsfor various resources. However, the distributed resource characteristicdata set is not accurate for purposes of generating a fair market offerset, as trusted sellers of used mobile devices generally receive ahigher price for a particular resource. Generating a trusted resourcecharacteristic data set via an exception detection model removes thedeficiencies of trusting either data set associated with the one or morethird-party entity/entities and associated with the distributed resourcecharacteristic data set.

At block 1252, the apparatus 900 includes means, such as data managementcircuitry 910, communications circuitry 908, processor 902, and/or thelike, or a combination thereof, for retrieving an untrusted third-partyresource characteristic data set. The untrusted third-party resourcecharacteristic data set may include one or more records associated withone or more third-party offerings of a resource by a third-party entity.For example, in some embodiments, the untrusted third-party resourcecharacteristic data set comprises a third-party resource pricing dataset. The third-party resource pricing data set may include one or morerecords, each including or otherwise associated with an offer price,resource set identifier, and/or timestamp. Each record may represent anoffer price for a particular resource set identifier offered by athird-party entity on a particular date. In some embodiments, theresources may be used mobile devices.

In some embodiments, the untrusted third-party characteristic data setmay be scraped, for example from one or more web services accessible viacommunications with a third-party device, such as a server, associatedwith the third-party entity. The apparatus 900 may include means toperform the scraping, and/or be associated with one or more systems forperforming the scraping, and retrieve the untrusted third-partycharacteristic data set from a repository updated upon completion of thescraping. In some embodiments, the untrusted third-party resourcecharacteristic data set may be retrieved from a third-party deviceassociated with the third-party entity. For example, via one or moreAPIs, the apparatus 900 may communicate with a server and/or accessiblerepository associated with the third-party entity to retrieve theuntrusted third-party resource characteristic data set. In otherembodiments, the untrusted third-party resource characteristic data setmay be retrieved from a third-party device associated with a differentthird-party entity, for example a data aggregator.

At block 1254, the apparatus 900 includes means, such as data managementcircuitry 910, communications circuitry 908, processor 902, and/or thelike, or a combination thereof, for retrieving a distributed resourcecharacteristic data set associated with a distributed user platform. Thedistributed resource characteristic data set may include one or morerecords associated with one or more distributed user generated offeringsof a resource provided via a distributed user platform. In someembodiments, a distributed user platform may be enable users to offer tobuy and/or sell resources, at any price desired by the user, to otherusers of the distributed user platform. Examples include, but are notlimited to, the distributed user platforms of eBay™, Craigslist®, AmazonMarketplace™, Facebook Marketplace™, or the like. Each record mayrepresent prices for used mobile devices offered by a user on aparticular date via a particular distributed user platform. Each recordmay include, or otherwise be associated with, for example, an offerprice, resource set identifier, and/or timestamp.

In some embodiments, the distributed resource data set may be scraped,for example from one or more web services accessible via communicationswith a device associated with the distributed user platform, such as aserver. The apparatus 900 may include means to perform the scraping,and/or be associated with one or more systems for performing thescraping, and retrieve the distributed resource characteristic data setfrom a repository updated upon completion of the scraping. In someembodiments, the distributed resource characteristic data set may beretrieved from a device associated with the distributed user platform.For example, via one or more APIs, the apparatus 900 may communicatewith a server and/or accessible repository associated with thedistributed user platform to retrieve the distributed resourcecharacteristic data set. In other embodiments, the distributed resourcecharacteristic data set may be retrieved from a device associated with adifferent third-party entity, for example a data aggregator.

The apparatus may generate a trusted resource characteristic data set byapplying at least the untrusted third-party resource characteristic dataset (or multiple untrusted third-party resource characteristic datasets) and the distributed resource characteristic data set to anexception detection model. The exception detection model may bedesigned, configured, and/or trained to detect outliers and/or otherexceptions associated with a particular characteristic orcharacteristics. The exception detection model may, in some embodiments,be embodied by one or more algorithms or machine learning models. Inthis regard, applying at least the untrusted third-party resourcecharacteristic data set and distributed resource characteristic data setto the exception detection model may comprise one or more of the blocks1256-1278.

At block 1256, the apparatus 900 includes means, such as modelperformance circuitry 912, processor 902, and/or the like, or acombination thereof, for integrating the untrusted third-party resourcecharacteristic data set and the distributed resource characteristic dataset. Integrating the untrusted third-party resource characteristic dataset and the distributed resource characteristic data set may compriseone or more pre-processing steps for aligning, organizing, and/orotherwise constructing the data sets for comparison.

In some embodiments, the untrusted third-party resource characteristicdata set and the distributed resource characteristic data set arealigned based on a temporal alignment. Using a third-party resourcepricing data set as an untrusted third-party resource characteristicdata set and a distributed resource pricing data set as the distributedresource characteristic data set, for example, the third-party resourcepricing data set may include, at least, a plurality records that eachinclude a resource price offered by the third-party and an associatedtimestamp (e.g., representing the date on which the price was offered bythe third-party). Similarly, the distributed resource pricing data setmay include a plurality of records that each include, at least, aresource price offered via a distributed user platform and an associatedtimestamp (e.g., representing the date on which the price was offeredvia the distributed user platform). An example temporal alignment mayalign the third-party resource pricing data set and the distributedresource pricing data set based on the timestamps for each record, forexample such that records associated with the same date may be compared.

In some embodiments, the untrusted third-party resource characteristicdata set and the distributed resource characteristic data set arealigned based on a temporal alignment and a resource set identifieralignment. Continuing the example of the third-party resource pricingdata set and the distributed resource pricing data set, each record inthe third-party resource pricing data set and the distributed resourcepricing data set may also include, or otherwise be associated with, aparticular resource set identifier. Based on the resource set identifierin or associated with each record, the untrusted third-party resourcedata set and the distributed resource pricing data set may be alignedsuch that records associated with the same date and the same resourceset identifier may be compared.

At block 1258, the apparatus 900 includes means, such as modelperformance circuitry 912, processor 902, and/or the like, or acombination thereof, for identifying an offset between the untrustedthird-party resource characteristic data set and the distributedresource characteristic data set. In some embodiments, the apparatusincludes means for comparing a first characteristic of a first resourcein the untrusted third-party resource characteristic data set with thefirst characteristic of the first resource in the distributed resourcecharacteristic data set from the distributed user platform to identifythe offset. In some embodiments, the first characteristic may be aresource price, for example where the untrusted third-party resourcedata set comprises a third-party resource pricing data set and thedistributed resource characteristic data set comprises a distributedresource pricing data set. In some such embodiments, the offset mayrepresent a difference in price for a particular resource set identifierfor a given time interval (e.g., for each day, each week, and the like)between the untrusted third-party resource characteristic data set andthe distributed resource characteristic data set.

At block 1260, the apparatus 900 includes means, such as modelperformance circuitry 912, processor 902, and/or the like, or acombination thereof, for identifying an exception period set, comprisingat least one exception period in the untrusted third-party resourcecharacteristic data set, based upon a deviation in the offset. Forexample, the deviation may be a change in the offset from an expected,determined, or average level. In some embodiments, for example, eachexception period may represent a time interval during which a particularresource set identifier is offered by the third-party entity at anelevated price (e.g., a promotional price). In this regard, eachexception period may be defined by a first timestamp (e.g., an intervalstart timestamp) and a second timestamp (e.g., an interval endtimestamp), where the exception period is flagged for all recordsassociated with intermediate timestamps between the first and secondtimestamps.

In some embodiments, an exception period may be identified when thedeviation in the offset satisfies an exception deviation threshold. Insome embodiments, the apparatus 900 may identify, determine, retrieve,or otherwise be associated with the exception deviation threshold. Insome embodiments, for example, the apparatus 900 may include means foridentifying a first timestamp at which the deviation of the offsetsatisfies the exception deviation threshold. For example, in someembodiments, the deviation of the offset satisfies the exceptiondeviation threshold when the deviation is greater than, or greater thanor equal to, the exception deviation threshold. Using a pricecharacteristic as an example, the exception deviation threshold may besatisfied when the deviation in the offset is above a certain value orpercentage, and is due to the resource price associated with theuntrusted third-party resource characteristic data set being above a setamount or a set percentage greater than the resource price associatedwith the distributed resource characteristic data set. The apparatus 900may include means for identifying a second timestamp at which thedeviation of the offset does not satisfy the exception deviationthreshold. In some embodiments, for example, the deviation may be adesired standard deviation amount from an expected or average deviationbased on historical pricing characteristics over a predeterminednon-exception time interval (e.g., 15 weeks, not including exceptionperiods).

Additionally, for example in some embodiments, the deviation of theoffset does not satisfy the exception deviation threshold when thedeviation is less than, or less than or equal to, the exceptiondeviation threshold. Returning to the price characteristic as anexample, the exception deviation threshold may not be satisfied when thedeviation in the offset returns to, or falls below, a certain value orpercentage, such as when the resource price associated with theuntrusted third-party resource characteristic data set returns to astandard operating range from the resource price associated with thedistributed characteristic data set. The price characteristic returningto within the standard operating range indicates the end an exceptionperiod, for example a promotional period.

At block 1262, the apparatus 900 includes means, such as modelperformance circuitry 912, processor 902, and/or the like, or acombination thereof, for removing the at least one exception period fromthe untrusted third-party resource characteristic data set to generatean updated untrusted third-party resource characteristic data set. Insome embodiments, removing the untrusted third-party resourcecharacteristic data set comprises marking each record associated withthe exception period as an exception, such that these records may beignored. By marking the exception periods, the untrusted third-partyresource characteristic data set may be used for data analysis, forexample by rendering indications of the untrusted third-party resourcecharacteristic data set to one or more interfaces provided to an offercontrol user and/or offer approval user. In other embodiments, therecords associated with the exception period may be deleted from theuntrusted third-party resource characteristic data set.

At block 1264, the apparatus 900 includes means, such as modelperformance circuitry 912, processor 902, and/or the like, or acombination thereof, for generating the trusted resource characteristicdata set based on at least the updated untrusted third-party resourcecharacteristic data set. In some embodiments, for example, the trustedresource characteristic data set may comprise the updated untrustedthird-party resource characteristic set. In other embodiments, thetrusted resource characteristic data set may comprise at least anaverage resource price characteristic for a given resource setidentifier by averaging the remaining price characteristic for eachrecord associated with the resource set identifier. The trusted resourcecharacteristic data set may include, for example, a maximum and/oraverage price characteristic for various resources or resource setidentifiers associated with offers by the third-party entity associatedwith the updated untrusted third-party resource characteristic data set.

In some embodiments, multiple untrusted third-party resourcecharacteristic data sets may be updated and compared, such thatgenerating the trusted resource characteristic data set is based on thecomparison of the multiple untrusted third-party resource characteristicdata sets. In this regard, at block 1266, the apparatus 900 includesmeans, such as data management circuitry 910, communications circuitry908, processor 902, and/or the like, or a combination thereof, forretrieving a second untrusted third-party resource characteristic dataset. The second untrusted third-party resource characteristic data setmay include one or more records associated with one or more third-partyofferings of a resource by a second third-party entity. For example, insome embodiments, the second entity may be a second commercial entitythat purchases used mobile devices.

In some embodiments, the second untrusted third-party characteristicdata set may be scraped, for example from one or more web servicesaccessible via communications with another third-party device, such as asecond server, associated with the second third-party entity. Theapparatus 900 may include means to perform the scraping, and/or beassociated with one or more systems for performing the scraping, andretrieve the second untrusted third-party characteristic data set from arepository updated upon completion of the scraping. In some embodiments,the second untrusted third-party resource characteristic data set may beretrieved from a second third-party device associated with the secondthird-party entity. For example, via one or more APIs, the apparatus 900may communicate with a second server and/or accessible second repositoryassociated with the second third-party entity to retrieve the seconduntrusted third-party resource characteristic data set. In otherembodiments, the second untrusted third-party resource characteristicdata set may be retrieved from a second third-party device associatedwith a different third-party entity, for example a data aggregator. Insome embodiments, the second untrusted third-party characteristic dataset may be retrieved in the same manner as the earlier, first retrieveduntrusted third-party characteristic data set. At block 1270, theapparatus 900 includes means, such as model performance circuitry 912,processor 902, and/or the like, or a combination thereof, foridentifying a second offset between the untrusted third-party resourcecharacteristic data set and the distributed resource characteristic dataset.

At block 1272, the apparatus 900 includes means, such as modelperformance circuitry 912, processor 902, and/or the like, or acombination thereof, for identifying a second exception period set,comprising at least one exception period in the second untrustedthird-party resource characteristic data set, based upon a seconddeviation in the second offset. For example, the second deviation may bea change in the offset from an expected, determined, or average levelbased on the distributed resource characteristic data set.

In some embodiments, an exception period in the second untrustedthird-party resource characteristic data set is identified when thesecond deviation in the second offset satisfies the exception deviationthreshold, or a second exception deviation threshold associated with thesecond untrusted third-party resource characteristic data set. It shouldbe appreciated that the offset and the deviation may define an expectedoperating range for the characteristic, for example a price range for aprice characteristic associated with a particular resource setidentifier.

At block 1274, the apparatus 900 includes means, such as modelperformance circuitry 912, processor 902, and/or the like, or acombination thereof, for removing the exception period set from thesecond untrusted third-party resource characteristic data set togenerate an updated second untrusted third-party resource characteristicdata set. In some embodiments, removing the exception period set fromthe second untrusted third-party resource characteristic data setcomprises marking as an exception each record in, or associated with,each exception period, such that these records may be ignored. Bymarking the exception periods, the second untrusted third-party resourcecharacteristic data set may be used for data analysis. In otherembodiments, the records in, or associated with, the exception periodsmay be deleted from the untrusted second third-party resourcecharacteristic data set.

At block 1276, the apparatus 900 includes means, such as modelperformance circuitry 912, processor 902, and/or the like, or acombination thereof, for comparing the updated untrusted third-partyresource characteristic data set with the updated second untrustedthird-party resource characteristic data set. In some embodiments, theupdated untrusted third-party resource characteristic data set and theupdated second untrusted third-party resource characteristic data setmay be compared to determine a greater characteristic, such as a greaterprice characteristic, for a particular resource set identifier betweenthe two data sets. In other embodiments, multiple untrusted third-partyresource characteristic data sets may be compared.

At block 1278, the apparatus 900 includes means, such as modelperformance circuitry 912, processor 902, and/or the like, or acombination thereof, for generating the trusted resource characteristicdata set based on the comparison of the updated untrusted third-partyresource characteristic data set with the updated second untrustedthird-party resource characteristic data set. In some embodiments, thetrusted resource characteristic data set may be generated to includecertain resource characteristics from each of the data sets based on thecomparison. For example, where the data sets including pricingcharacteristics for resources, the trusted resource characteristic dataset may include the greatest pricing characteristic for each resourceset identifier based on the comparison between the two or more updateduntrusted third-party resource data sets.

For example, in some embodiments, the trusted resource characteristicdata set includes a fair market offer data object for various resourceset identifiers. The fair market offer data object may include a pricingcharacteristic, such as a fair market offer value, for each resource setidentifier, where the pricing characteristic is generated based on thecomparison. For example, the pricing characteristic for a particularresource set identifier may be a maximum pricing characteristic betweenthe various updated untrusted third-party resource characteristic datasets for the resource set identifier. The updated untrusted third-partyresource characteristic data set may include the maximum pricingcharacteristic for a particular resource set identifier associated withvarious third-party entities. For example, an average pricingcharacteristic may be determined, for a particular third-party entityand particular resource set identifier for example, by calculating theaverage pricing characteristic for the resource set identifier over apre-determined time interval (e.g., 15 weeks) with exception periodsremoved. The average pricing characteristic for a distributed userplatform may then be determined, for example based on the distributedresource characteristic data set. The maximum pricing characteristic forthe particular resource set identifier and for the particularthird-party entity may then be determined by multiplying the averagepricing characteristic for the resource set identifier associated withthe distributed user platform by the average pricing characteristic forthe resource set identifier associated with the third-party entity as apercentage of the average pricing characteristic for the resource setidentifier associated with the distributed user platform (e.g., theaverage pricing characteristic for the resource set identifierassociated with the third-party entity divided by the average pricingcharacteristic for the resource set identifier associated with thedistributed user platform).

The maximum pricing characteristic for each resource set identifier andeach third-party entity, represented in each of the updated untrustedresource characteristic data sets, may then be used to calculate a fairmarket offer value for a resource set identifier, which may be embodiedby a fair market offer data object and included in the trusted resourcecharacteristic data set associated with the particular resource setidentifier. For example, the fair market offer value (e.g., a trustedpricing characteristic) may be determined as the maximum pricingcharacteristic of the maximum pricing characteristics for eachthird-party entity. Continuing the example of used mobile deviceacquisition, if a first updated untrusted third-party resourcecharacteristic data set for third-party entity A was associated with apricing characteristic of 90 units (e.g., dollars for example) for aparticular resource set identifier, a second updated untrustedthird-party resource characteristic data set for a third-party entity Bwas associated with a pricing characteristic of 85 units for theparticular resource set identifier, a third updated untrustedthird-party resource characteristic data set for a third-party entity Cwas associated with a pricing characteristic of 87 units for theparticular resource set identifier, and a fourth updated untrustedthird-party resource characteristic data set for a third-party entity Dwas associated with a pricing characteristic of 93 units for theparticular resource set identifier, the trusted resource characteristicdata set may include a pricing characteristic for the particularresource set identifier of 93 units, as the maximum between all updateduntrusted third-party resource characteristic data sets. This pricingcharacteristic may be embodied as a fair market offer data object forthe particular resource set identifier, representing the fair marketoffer value for resources associated with the particular resource setidentifier during non-exception (e.g., non-promotional) periods.

The process 1250B for generating a trusted resource characteristic dataset similarly enables generation of a trusted characteristic set forother non-resource data sets from one or more untrusted data sets. Forexample, an untrusted characteristic data set may be retrieved, wherethe untrusted characteristic set is associated with an untrusted,third-party entity. A distributed characteristic data set may then becollected from, or associated with, a distributed user platform. Anoffset may then be identified between the untrusted third-partycharacteristic data set and the distributed characteristic data set.Exception periods may be identified based on a deviation in the offset,and the exception periods may be removed from the untrustedcharacteristic data set to generate an updated untrusted resourcecharacteristic data set. The updated untrusted resource characteristicdata set may then be used to generate the trusted characteristic dataset, and/or multiple updated untrusted resource characteristic data setsmay be generated such that the trusted characteristic data set may begenerated based on a comparison between the multiple updated untrustedresource characteristic data sets. The use of resource pricing in theabove description should not be considered to limit the scope and spiritof the disclosure herein.

Example User Interfaces

FIGS. 13-15 illustrate example embodiment user interfaces. For example,some systems, methods, and computer program products may be configuredto render, or otherwise cause rendering, of one or more of the exampleinterfaces. It should be appreciated that, in some embodiments thevarious components illustrated in each interface could be embodied by anumber of known interface components configured to receive a myriad ofuser input types. All interface components, alone and in combination,are illustrative and not to limit the scope and spirit of the disclosureherein.

In some embodiments, each of the interfaces may be rendered by a clientdevice in response to receiving a control signal comprising a renderabledata object. The control signal may be generated and/or configured by aresource offer generation system, for example, for transmission to oneor more client device. In some embodiments, the renderable data objectis generated and/or configured by the resource offer generation system,for example, to include the interface to be rendered.

FIG. 13 illustrates an example offer adjustment interface 1300 inaccordance with embodiments of the present disclosure. The offeradjustment interface 1300 may be rendered, for example caused by aresource offer generation system upon generation of a resource offerset, to a client device associated with an offer control user. The offeradjustment interface 1300 comprises an offer analysis table 1322. Theoffer analysis table 1322 may comprise a row for each resource offerdata object in a generated resource offer set. The offer analysis table1322 comprises a plurality of columns of information associated with,and including, the generated resource offer set. The offer analysistable comprises the resource offer value column 1302 comprising theresource offer values for each resource offer data object in thegenerated resource offer set. Each row of the resource offer column 1302is configured to receive user input for adjusting the correspondingresource offer value of the resource offer data object. For example, anoffer control user may engage a particular row to input a new resourceoffer value for the particular resource offer data object.

An offer analysis table may further include one or more additionalcolumns of information associated with analyzing the resource offer set.For example, the offer analysis table 1322 includes resource attributedata columns 1306, market intelligence data 1308, and system generateddata columns 1310. The system generated data columns, such as systemgenerated data columns 1310, include data generated and outputted by oneor more of a prediction system, such as prediction system 102 embodiedby apparatus 200, and/or a resource offer generation system, such asresource offer generation system 802 embodied by apparatus 900. In someembodiments, one or more system generated data columns may includeinformation derived and/or calculated based on data generated andoutputted by one or more of a prediction system, such as predictionsystem 102 embodied by apparatus 200, and/or a resource offer generationsystem, such as resource offer generation system 802 embodied byapparatus 900, for example in combination with market intelligence data,such as an expected margin associated with each resource offer dataobject.

The offer adjustment interface 1300 includes an indication of an offeranalytics data set 1304, specifically at least a portion of the offeranalytics data set rendered as text. The indication of an offeranalytics data set may be rendered non-overlapping from the offeranalysis table 1322 and dashboard 1320, to enable dynamic and efficientvisualization and analysis while navigating the offer analysis table1322 and/or performing adjustments. The offer analytics data set mayinclude various information associated with the generated resource offerset and/or adjusted resource offer set as currently adjusted. Forexample, the offer analytics data set may include a profit-per-resourcederived from the generated resource offer set. Additionally oralternatively, in some embodiments, the offer analytics data set furtherincludes a profit margin for the adjusted resource offer set ascurrently adjusted. Additionally or alternatively, the offer analyticsdata set may include a resource loss indicator representing the numberof resource offer data objects currently associated with a negativemargin value (e.g., associated with an expected sale price that does notexceed the resource offer value). In some embodiments, at least aportion of the offer analytics data set is dynamically updated as anoffer control user adjusts one or more resource offer values for variousresource offer data objects in the resource offer set. The indication ofthe offer analytics data set may also be updated to reflect the updatedoffer analytics data set.

The offer analysis interface 1300 includes analysis table managementcomponents 1314. Each of the analysis table management components 1314may be configured to filter, adjust, or otherwise affect the datarendered via offer analysis table 1322. For example, one or moreanalysis table management components may be provided to filter the rowsbased on a particular value for a particular column, such as based on aresource set identifier or other resource attribute (e.g., carrier,make, model/category type, and the like).

The offer analysis interface includes offer saving component 1316. Theoffer saving component 1316 may be configured to enable saving of anadjusted resource offer set without submitting it for approval. Forexample, the offer saving component 1316 may be configured to causetransmission, for example to a resource offer generation system 802, ofa request for storing the adjusted resource offer set accessible by theoffer control user. When saved, the adjusted resource offer set may belater retrieved and used when rendering the offer adjustment interface(e.g., in another session).

The offer analysis interface includes offer submitting component 1318.The offer submitting component 1318 may be configured to enablesubmitting of an adjusted resource offer set for approval by an offerapproval user. The adjusted resource offer set may comprise the resourceoffer data objects as adjusted by the offer control user via the offeradjustment interface. The adjusted offer set may include one or moreresource offer data objects having an adjusted resource offer value. Toenable submitting of the adjusted resource offer set, the offersubmitting component 1318 may, for example, be configured to causetransmission, such as to a resource offer generation system 802, of theadjusted resource offer set. The adjusted resource offer set may betransmitted as part of, or otherwise associated with, a request forstoring the submitted adjusted resource offer set.

The offer analysis interface includes external management components1324. The external management components may be configured forgenerating and/or managing one or more files representing modifying theoffer analysis table 1322. For example, external management components1324 may include one or more components for uploading a file comprisingat least a resource offer set, such as a Microsoft Excel™, for renderingvia an offer analysis table. External management components 1324 mayadditionally or alternatively include one or more components fordownloading the offer analysis table 1322, or a portion thereof, to afile. For example, the offer analysis table 1322 may, if needed, beconverted to an external file type (e.g., Microsoft Excel™) anddownloaded.

Offer adjustment interface 1300 further includes a dashboard portion1320. The dashboard portion 1320 may be rendered non-overlapping fromthe indication of the offer analytics data set 1304, and the offeranalysis table 1322 and dashboard 1320, to enable efficientvisualization and analysis while navigating the interfaces offered bydashboard portion 1320. The dashboard portion 1320 may include one ormore components for accessing one or more other interfaces associatedwith the resource offer set and/or market intelligence data. Thedashboard portion 1320 specifically includes a user interface componentfor accessing, or otherwise causing rendering of, an offer strengthinterface, market comparison interface, and price strength interface.

FIG. 14 illustrates an example offer approval interface 1400 inaccordance with embodiments of the present disclosure. The offerapproval interface 1400 may be rendered, for example caused by aresource offer generation system upon submission of an adjusted resourceoffer set by an offer control user, to an approval device associatedwith an offer approval user. The offer approval interface 1400 comprisesthe offer analysis table 1322, which includes the resource offer valuecolumn 1302 and remaining columns 1306-1312. The resource offer valuecolumn 1302 may be rendered such that it is not adjustable. For examplethe resource offer value column 1302 may not be configured to receiveuser input. The offer approval interface 1400 may additionally includethe dashboard portion 1320, for accessing one or more of the variousother interfaces described, and the price analytics information 1304based on the adjusted resource offer set.

The offer approval interface 1400 includes offer approval component 1402and offer rejection component 1404. The offer approval component 1402enables approval of the adjusted resource offer set. For example, inresponse to user engagement by an offer approval user with the offerapproval component 1402, the approval device may transmit an offerapproval response comprising an offer approval status representing anapproved status. The offer rejection component 1404 enables rejection ofthe adjusted resource offer set. For example, in response to userengagement by an offer approval user with the offer rejection component1404, the approval device may transmit an offer approval responsecomprising an offer approval status representing a rejected status. Insome embodiments, in response to user engagement by an offer approvaluser with the offer rejection component 1404, the approval device maycause rendering of an interface component (not shown) configured tocreate and submit an offer rejection message. For example, a text boxconfigured to enable the offer approval user to create the offerrejection message, and message submit button where, upon user engagementwith the message submit button, the admin device transmits an offerapproval response including at least an offer approval statusrepresenting a rejected status, and the created offer rejection message.

FIG. 15 illustrates an example market comparison interface 1500 inaccordance with embodiments of the present disclosure. The marketcomparison interface 1500 may be rendered, for example caused by aresource offer generation system, to a user device or an approval deviceupon engagement with an interface component associated with thedashboard portion 1320. The market comparison interface 1500 includesthe dashboard portion 1320, for accessing one or more of the variousother interfaces described.

Market comparison interface 1500 comprises competitor selectioncomponents 1502. The competitor selection components may be configuredfor toggling between summarizing market data based on comparison toresource set identifiers marked as in a promotion period, comparison toresource set identifiers market not market as in a promotion period, orall resource set identifier. The component status of each of thecompetitor selection components 1502 may filter market intelligence dataused to generate the market summary visualization components 1506 andmarket summary table 1508.

Market comparison interface 1500 comprises data management components1504. Data management components 1504 may include one or more interfacecomponents for receiving user input for one or more resource attributes.The input resource attribute values may be used to filter, or furtherfilter, market intelligence data used to generate the market summaryvisualization components 1506 and market summary table 1508.

Market comparison interface 1500 includes market summary visualizationcomponents 1506. The market summary visualization components 1506 mayprovide a summary of the resource offer values for resource offer dataobjects of a particular adjusted resource offer set. For example, marketsummary visualization component 1506A may provide a summary of allresource offer values compared to the market average for thecorresponding resource set identifier, based on the market intelligencedata for all competitor entities. Market summary visualization component1506B may provide a summary of all resource offer values compared to themarket maximum for the corresponding resource set identifier (e.g., fora particular resource offer data object having a resource offer valuefor a particular resource set identifier, the highest offer valueassociated with a competitor entity for that resource set identifier),based on the market intelligence data for all competitor entities'visualization component.

The market comparison interface 1500 comprises market summary table1508. The market summary table 1508 may comprise aggregated summaries ofmarket intelligence data associated with all competitor entities. Forexample, the market summary table 1508 may include the number ofresources within predefined bands compared to a reference metric. Forexample, the number of resources associated with resource offer dataobjects having resource offer values within a predefined range,represented by the predefined band, may be displayed. The bands may bedetermined based on the region-program identifier for the selectedregion-program data object.

The dashboard, such as dashboard 1320 in FIGS. 13, 14, and 15, may alsoprovide access to a price trends interface. The price trends interfacemay include various visual indications, such as graphs, associated withthird-party offer values associated with a third-party compared to theaverage sales price for a particular channel profile associated with adistributed user platform, such as eBay™ or the like. The price trendsinterface may include such indications for any number of third-parties(e.g., one or more third-parties, one or more competitors, or the like).Further, the price trends interface may render indications for promotionperiods.

Additional Implementation Details

Although an example processing system has been described in FIG. 2,implementations of the subject matter and the functional operationsdescribed herein can be implemented in other types of digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them.

Embodiments of the subject matter and the operations described hereincan be implemented in digital electronic circuitry, or in computersoftware, firmware, or hardware, including the structures disclosed inthis specification and their structural equivalents, or in combinationsof one or more of them. Embodiments of the subject matter describedherein can be implemented as one or more computer programs, i.e., one ormore modules of computer program instructions, encoded on computerstorage medium for execution by, or to control the operation of,information/data processing apparatus. Alternatively, or in addition,the program instructions can be encoded on an artificially-generatedpropagated signal, e.g., a machine-generated electrical, optical, orelectromagnetic signal, which is generated to encode information/datafor transmission to suitable receiver apparatus for execution by aninformation/data processing apparatus. A computer storage medium can be,or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially-generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate physical components or media (e.g., multiple CDs, disks, orother storage devices).

The operations described herein can be implemented as operationsperformed by an information/data processing apparatus oninformation/data stored on one or more computer-readable storage devicesor received from other sources.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing. The apparatus can includespecial purpose logic circuitry, e.g., an FPGA (field programmable gatearray) or an ASIC (application-specific integrated circuit). Theapparatus can also include, in addition to hardware, code that createsan execution environment for the computer program in question, e.g.,code that constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more of them.The apparatus and execution environment can realize various differentcomputing model infrastructures, such as web services, distributedcomputing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor information/data (e.g., one or more scripts stored in a markuplanguage document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub-programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described herein can be performed by oneor more programmable processors executing one or more computer programsto perform actions by operating on input information/data and generatingoutput. Processors suitable for the execution of a computer programinclude, by way of example, both general and special purposemicroprocessors, and any one or more processors of any kind of digitalcomputer. Generally, a processor will receive instructions andinformation/data from a read-only memory or a random access memory orboth. The essential elements of a computer are a processor forperforming actions in accordance with instructions and one or morememory devices for storing instructions and data. Generally, a computerwill also include, or be operatively coupled to receive information/datafrom or transfer information/data to, or both, one or more mass storagedevices for storing data, e.g., magnetic, magneto-optical disks, oroptical disks. However, a computer need not have such devices. Devicessuitable for storing computer program instructions and information/datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described herein can be implemented on a computer having adisplay device, e.g., a CRT (cathode ray tube) or LCD (liquid crystaldisplay) monitor, for displaying information/data to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

Embodiments of the subject matter described herein can be implemented ina computing system that includes a back-end component, e.g., as aninformation/data server, or that includes a middleware component, e.g.,an application server, or that includes a front-end component, e.g., aclient computer having a graphical user interface or a web browserthrough which a user can interact with an implementation of the subjectmatter described herein, or any combination of one or more suchback-end, middleware, or front-end components. The components of thesystem can be interconnected by any form or medium of digitalinformation/data communication, e.g., a communication network. Examplesof communication networks include a local area network (“LAN”) and awide area network (“WAN”), an inter-network (e.g., the Internet), andpeer-to-peer networks (e.g., ad hoc peer-to-peer networks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits information/data (e.g., an HTML page) toa client device (e.g., for purposes of displaying information/data toand receiving user input from a user interacting with the clientdevice). Information/data generated at the client device (e.g., a resultof the user interaction) can be received from the client device at theserver.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments of particular inventions.Certain features that are described herein in the context of separateembodiments can also be implemented in combination in a singleembodiment. Conversely, various features that are described in thecontext of a single embodiment can also be implemented in multipleembodiments separately or in any suitable sub-combination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular embodiments of the subject matter have been described.Other embodiments are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

CONCLUSION

Many embodiments of the subject matter described may include all, orportions thereof, or a combination of portions, of the systems,apparatuses, methods, and/or computer program products described herein.The subject matter described herein includes, but is not limited to, thefollowing specific embodiments:

1. A method for allocating a constrained resources set in a dynamicenvironment, the method comprising:

receiving, from a client device associated with a channel profile, arequest data object;

receiving a tiering parameters data object;

receiving a decay parameters data object;

extracting, from the request data object, a resource request set,wherein the resource request set comprises a plurality of requestparameters;

extracting, from the tiering parameters data object, a plurality oftiering parameters;

extracting, from the decay parameters data object, a plurality of decayparameters;

assigning the channel profile to a first tier from amongst a pluralityof tiers, wherein assigning the channel profile to the first tiercomprises applying the plurality of tiering parameters and a firstrequest parameter from the plurality of request parameters to a firstmodel;

generating an adjusted resource request set associated with the user byapplying a decay curve to a second request parameter from the pluralityof request parameters, wherein the decay curve is based at least in parton the plurality of decay parameters;

determining, based on the assigned first tier and the adjusted resourcerequest set, if the channel profile satisfies each of plurality ofthreshold conditions;

in response to determining that the channel profile satisfies each ofthe plurality of threshold conditions, applying the adjusted resourcerequest set and the assigned tier to a second model to generate aresource allocation set for the channel profile; and

generating a control signal causing a renderable object comprising anindication of the resource allocation set to be displayed on a userinterface.

2. The method of embodiment 1, wherein the plurality of tieringparameters comprises a portfolio-level volume associated with a channelprofile.

3. The method of embodiment 2, further comprising scaling theportfolio-level volume associated with the channel profile based atleast in part on assigning the portfolio-level volume associated withthe channel profile to a position in a ranked list of portfolio-levelvolumes.

4. The method of any one of embodiments 1-3, wherein the plurality oftiering parameters comprises a projected portfolio-level profit marginassociated with a channel profile.

5. The method of embodiment 4, further comprising scaling the projectedportfolio-level profit margin associated with the channel profile basedat least in part on assigning the projected portfolio-level profitmargin associated with the channel profile to a position in a rankedlist of projected portfolio-level profit margins.

6. The method of any one of embodiments 1-5, wherein the plurality oftiering parameters comprises an entropy parameter associated with achannel profile.

7. The method of embodiment 6, wherein the entropy parameter associatedwith the channel profile is expressed by the formula E=Σn*log n, where Eis the entropy parameter and n is the volume of devices bid in a givenbid, divided by the total volume of devices bid.

8. The method of embodiment 7, further comprising scaling the entropyparameter associated with the channel profile based at least in part onassigning the entropy parameter associated with the channel profile to aposition in a ranked list of entropy parameters.

9. The method of any one of embodiments 1-8, wherein the plurality oftiering parameters comprises an indication of a geographic locationassociated with a channel profile.

10. The method of any one of embodiments 1-9, wherein the plurality oftiering parameters comprises a timing parameter associated with arelationship between a channel profile and a first entity.

11. The method of embodiment 10, further comprising scaling the timingparameter based at least in part calculating a number of days reflectedby the timing parameter and assigning the calculated number of days to aposition in a ranked list of timing parameters.

12. The method of any one of embodiments 1-11, wherein the plurality oftiering parameters comprises an indication of an audit status of achannel profile.

13. The method of embodiment 12, further comprising scaling theindication of the audit status of the channel profile by at leastconverting the indication of the audit status of the channel profile toa single-digit binary value.

14. The method of any one of embodiments 1-13, wherein the plurality oftiering parameters comprises an indication of an exclusivity status of achannel profile.

15. The method of embodiment 14, further comprising scaling theindication of the exclusivity status of the channel profile by at leastconverting the indication of the exclusivity status of the channelprofile to a single-digit binary value.

16. The method of any one of embodiments 1-15, wherein the plurality ofdecay parameters comprises a set of historical pricing informationassociated with a plurality of channel profiles.

17. The method of any one of embodiments 1-16, wherein the plurality ofdecay parameters comprises a set of historical pricing informationassociated with a public auction market.

18. The method of any one of embodiments 1-17, wherein the plurality ofrequest parameters comprises a requested quantity of an inventoryelement.

19. The method of any one of embodiments 1-18, wherein the plurality ofrequest parameters comprises a first requested quantity of a firstinventory element.

20. The method of any one of embodiments 1-19, wherein the plurality ofrequest parameters comprises a plurality of requested quantities of aplurality of inventory elements.

21. The method of any one of embodiments 1-20, wherein the plurality ofrequest parameters comprises a list of SKU identifiers associated with aplurality of inventory elements.

22. The method of any one of embodiments 1-21, wherein the plurality ofrequest parameters comprises a first bid price for a first inventoryelement.

23. The method of any one of embodiments 1-22, wherein the plurality ofrequest parameters comprises a plurality of bids associated with aplurality of inventory elements.

24. The method of any one of embodiments 1-23, wherein the plurality ofrequest parameters comprises a set of properties associated with achannel profile.

25. The method of any one of embodiments 1-24, wherein assigning thechannel profile to the first tier from amongst a plurality of tiers,wherein assigning the channel profile to the first tier comprisesapplying the plurality of tiering parameters and the first requestparameter from the plurality of request parameters to a first modelcomprises:

determining whether a parameter within the plurality of tieringparameters comprises an outlier; and

removing the outlier from the plurality of tiering parameters.

26. The method of any one of embodiments 1-25, wherein generating theadjusted resource request set associated with the user by applying thedecay curve to the second request parameter from the plurality ofrequest parameters, wherein the decay curve is based at least in part onthe plurality of decay parameters comprises applying the plurality ofdecay parameters to a multivariate adaptive regression splines (MARS)model.

27. The method of any one of embodiments 1-26, wherein the second modelis configured to determine a plurality of probabilities associated withthe channel profile and the resource allocation set.

28. The method of any one of embodiments 1-27, further comprisinggenerating a control signal causing the renderable object comprising theindication of the resource allocation set to be displayed on a userinterface of the client device.

29. An apparatus for determining a predicted future demand for resourcesin a dynamic environment, the apparatus comprising at least oneprocessor and at least one memory comprising computer program code, theat least one memory and the computer program code configured to, withthe at least one processor, cause the apparatus to:

receive, from a client device associated with a channel profile, arequest data object;

receive a tiering parameters data object;

receive a decay parameters data object;

extract, from the request data object, a resource request set, whereinthe resource request set comprises a plurality of request parameters;

extract, from the tiering parameters data object, a plurality of tieringparameters;

extract, from the decay parameters data object, a plurality of decayparameters;

assign the channel profile to a first tier from amongst a plurality oftiers, wherein assigning the channel profile to the first tier comprisesapplying the plurality of tiering parameters and a first requestparameter from the plurality of request parameters to a first model;

generate an adjusted resource request set associated with the user byapplying a decay curve to a second request parameter from the pluralityof request parameters, wherein the decay curve is based at least in parton the plurality of decay parameters;

determine, based on the assigned first tier and the adjusted resourcerequest set, if the channel profile satisfies each of plurality ofthreshold conditions;

in response to determining that the channel profile satisfies each ofthe plurality of threshold conditions, apply the adjusted resourcerequest set and the assigned tier to a second model to generate aresource allocation set for the channel profile; and

generate a control signal causing a renderable object comprising anindication of the resource allocation set to be displayed on a userinterface.

30. The apparatus of embodiment 29, the at least one memory and thecomputer program code configured to, with the at least one processor,cause the apparatus to:

-   -   assign the channel profile to the first tier from amongst a        plurality of tiers, wherein assigning the channel profile to the        first tier comprises applying the plurality of tiering        parameters and the first request parameter from the plurality of        request parameters to a first model comprises:        -   determining whether a parameter within the plurality of            tiering parameters comprises an outlier; and        -   removing the outlier from the plurality of tiering            parameters.

31. The apparatus of any one of embodiments 29 or 30, the at least onememory and the computer program code configured to, with the at leastone processor, cause the apparatus to:

-   -   generate the adjusted resource request set associated with the        user by applying the decay curve to the second request parameter        from the plurality of request parameters, wherein the decay        curve is based at least in part on the plurality of decay        parameters comprises applying the plurality of decay parameters        to a multivariate adaptive regression splines (MARS) model.

32. A computer program product comprising at least one non-transitorycomputer-readable storage medium having computer-executable program codeinstructions stored therein, the computer-executable program codeinstructions comprising program code instructions configured to:

receive, from a client device associated with a channel profile, arequest data object;

receive a tiering parameters data object;

receive a decay parameters data object;

extract, from the request data object, a resource request set, whereinthe resource request set comprises a plurality of request parameters;

extract, from the tiering parameters data object, a plurality of tieringparameters;

extract, from the decay parameters data object, a plurality of decayparameters;

assign the channel profile to a first tier from amongst a plurality oftiers, wherein assigning the channel profile to the first tier comprisesapplying the plurality of tiering parameters and a first requestparameter from the plurality of request parameters to a first model;

generate an adjusted resource request set associated with the user byapplying a decay curve to a second request parameter from the pluralityof request parameters, wherein the decay curve is based at least in parton the plurality of decay parameters;

determine, based on the assigned first tier and the adjusted resourcerequest set, if the channel profile satisfies each of plurality ofthreshold conditions;

in response to determining that the channel profile satisfies each ofthe plurality of threshold conditions, apply the adjusted resourcerequest set and the assigned tier to a second model to generate aresource allocation set for the channel profile; and

generate a control signal causing a renderable object comprising anindication of the resource allocation set to be displayed on a userinterface.

33. The computer program product of embodiment 32, thecomputer-executable program code instructions comprising program codeinstructions configured to:

-   -   assign the channel profile to the first tier from amongst a        plurality of tiers, wherein assigning the channel profile to the        first tier comprises applying the plurality of tiering        parameters and the first request parameter from the plurality of        request parameters to a first model comprises:        -   determining whether a parameter within the plurality of            tiering parameters comprises an outlier; and        -   removing the outlier from the plurality of tiering            parameters.

34. The computer program product of any one of embodiments 32 or 33, thecomputer-executable program code instructions comprising program codeinstructions configured to:

-   -   generate the adjusted resource request set associated with the        user by applying the decay curve to the second request parameter        from the plurality of request parameters, wherein the decay        curve is based at least in part on the plurality of decay        parameters comprises applying the plurality of decay parameters        to a multivariate adaptive regression splines (MARS) model.

35. A method for determining a predicted future demand for resources ina dynamic environment, the method comprising:

receiving a request data object from a client device associated with auser;

extracting, from the request data object, a request data set, whereinthe request data set is associated with a first set of resources;

receiving a first context data object, wherein the first context dataobject is associated with one or more resource distribution channels;

retrieving a predicted channel and condition data set, whereinretrieving the predicted channel and condition data set comprisesapplying the request data set and the first context data object to afirst model; and

generating a control signal causing a renderable object comprising thepredicted channel and condition data set to be displayed on a userinterface of the client device associated with the user.

36. An apparatus for determining a predicted future demand for resourcesin a dynamic environment, the apparatus comprising at least oneprocessor and at least one memory comprising computer program code, theat least one memory and the computer program code configured to, withthe at least one processor, cause the apparatus to:

receive a request data object from a client device associated with auser;

extract, from the message request data object, a request data set,wherein the request data set is associated with a first set ofresources;

receive a first context data object, wherein the first context dataobject is associated with one or more resource distribution channels;

retrieve a predicted channel and condition data set, wherein retrievingthe predicted channel and condition data set comprises applying therequest data set and the first context data object to a first model; and

generate a control signal causing a renderable object comprising thepredicted channel and condition data set to be displayed on a userinterface of the client device associated with the user.

37. A computer program product comprising at least one non-transitorycomputer-readable storage medium having computer-executable program codeinstructions stored therein, the computer-executable program codeinstructions comprising program code instructions configured to:

-   -   receive a request data object from a client device associated        with a user;    -   extract, from the message request data object, a request data        set, wherein the request data set is associated with a first set        of resources;    -   receive a first context data object, wherein the first context        data object is associated with one or more resource distribution        channels;    -   retrieve a predicted channel and condition data set, wherein        retrieving the predicted channel and condition data set        comprises applying the request data set and the first context        data object to a first model; and    -   generate a control signal causing a renderable object comprising        the predicted channel and condition data set to be displayed on        a user interface of the client device associated with the user.

38. A computer-implemented method for generating a resource offer set,the method comprising:

-   -   retrieving at least one resource offer generation input data        set;    -   receiving a benchmark and portfolio target data set in response        to an input by an offer control user via one or more client        devices;    -   generating a resource offer set by applying at least one of the        at least one resource offer generation input data set and the        benchmark and portfolio target data set to a resource offer        generation model,    -   wherein the generated resource offer set satisfies the benchmark        and portfolio target data set;    -   generating a control signal causing a renderable object        comprising an offer adjustment interface displayed at a first of        the one or more client devices and configured for updating the        resource offer set to create an adjusted resource offer set, the        offer adjustment interface comprising an indication of the        resource offer set;    -   receiving a completion control signal from the first of the one        or more client devices;    -   in response to the completion control signal, generating an        approval request control signal causing a second renderable data        object comprising an approval interface to be displayed at a        second of the one or more client devices, wherein the approval        interface comprises an indication of the adjusted resource offer        set;    -   receiving, from the second of the one or more client devices, an        offer approval control signal comprising an offer status        indicator; and    -   storing the resource offer set associated with the offer status        indicator.

39. The computer-implemented method of embodiment 38, the method furthercomprising:

-   -   receiving a region-program identifier via one or more client        devices;    -   receiving a collection period data object associated with the        region-program identifier via the one or more client devices;        and    -   validating the collection period data object by comparing the        collection period data object to a valid timestamp range object,    -   wherein storing the resource offer set is associated with the        offer status indicator, the collection period data object, and        the region-program identifier.

40. The computer-implemented method of any one of embodiments 38 or 39,the method further comprising:

-   -   receiving control signals, from the first of the one or more        client devices, comprising one or more adjustment data objects;        and    -   updating the resource offer set based on the one or more        adjustment data objects to create the adjusted resource offer        set.

41. The computer-implemented method of any one of embodiments 38-40,wherein the adjusted resource offer set comprises the resource offerset.

42. The computer-implemented method of any one of embodiments 38-41,wherein retrieving the at least one resource offer generation input dataset comprises:

-   -   retrieving at least one updated resource offer generation input        data set, wherein the at least one resource offer generation        input data set comprises the at least one updated resource offer        generation input data set.

43. The computer-implemented method of any one of embodiments 38-42,wherein retrieving the at least one resource offer generation input dataset comprises:

-   -   determining at least one resource offer generation input data        set satisfies an untrustworthiness threshold; and    -   retrieving an updated resource offer generation input data set        for the at least one resource offer generation input data set        for including in the resource offer generation input data set.

44. The computer-implemented method of any one of embodiments 38-43,wherein the benchmark and portfolio target data set comprises at leastone data object representing a boundary condition, and wherein theresource offer set satisfies the benchmark and portfolio target data setby satisfying the at least one boundary condition.

45. The computer-implemented method of any one of embodiments 38-44,wherein the offer adjustment interface further comprises an indicationof an offer analytics data set generated based on the resource offer setand at least one of the at least one resource offer generation inputdata set.

46. An apparatus for generating a resource offer set, the apparatuscomprising at least one processor and at least one memory comprisingcomputer program code, the at least one memory and the computer programcode configured to, with the at least one processor, cause the apparatusto:

-   -   retrieve at least one resource offer generation input data set;    -   receive a benchmark and portfolio target data set in response to        an input by an offer control user via one or more client        devices;    -   generate a resource offer set by applying at least one of the at        least one resource offer generation input data set and the        benchmark and portfolio target data set to a resource offer        generation model,    -   wherein the generated resource offer set satisfies the benchmark        and portfolio target data set;    -   generate a control signal causing a renderable object comprising        an offer adjustment interface displayed at a first of the one or        more client devices and configured for updating the resource        offer set to create an adjusted resource offer set, the offer        adjustment interface comprising an indication of the resource        offer set;    -   receive a completion control signal from the first of the one or        more client devices;    -   in response to the completion control signal, generating an        approval request control signal causing a second renderable data        object comprising an approval interface to be displayed at a        second of the one or more client devices, wherein the approval        interface comprises an indication of the adjusted resource offer        set;    -   receive, from the second of the one or more client devices, an        offer approval control signal comprising an offer status        indicator; and    -   store the resource offer set associated with the offer status        indicator.

47. The apparatus of embodiment 46, the at least one memory and thecomputer program code further configured to, with the at least oneprocessor, cause the apparatus to:

-   -   receive a region-program identifier via one or more client        devices;    -   receive a collection period data object associated with the        region-program identifier via the one or more client devices;        and    -   validate the collection period data object by comparing the        collection period data object to a valid timestamp range object,    -   wherein the apparatus is configured to store the resource offer        set associated with the offer status indicator, the collection        period data object, and the region-program identifier.

48. The apparatus of any one of embodiments 46 or 47, the at least onememory and the computer program code further configured to, with the atleast one processor, cause the apparatus to:

-   -   receive control signals, from the first of the one or more        client devices, comprising one or more adjustment data objects;        and    -   update the resource offer set based on the one or more        adjustment data objects to create the adjusted resource offer        set.

49. The apparatus of any one of embodiments 46-48, wherein the adjustedresource offer set comprises the resource offer set.

50. The apparatus of any one of embodiments 46-49, wherein, to retrievethe at least one resource offer generation input data set, the computerprogram code configures the apparatus to:

-   -   retrieve at least one updated resource offer generation input        data set, wherein the at least one resource offer generation        input data set comprises the at least one updated resource offer        generation input data set.

51. The apparatus of any one of embodiments 46-50, wherein, to retrievethe at least one resource offer generation input data set, the computerprogram code configures the apparatus to:

-   -   determining at least one resource offer generation input data        set satisfies an untrustworthiness threshold; and    -   retrieving an updated resource offer generation input data set        for the at least one resource offer generation input data set        for including in the resource offer generation input data set.

52. The apparatus of any one of embodiments 46-51, wherein the benchmarkand portfolio target data set comprises at least one data objectrepresenting a boundary condition, and wherein the resource offer setsatisfies the benchmark and portfolio target data set by satisfying theat least one boundary condition.

53. The apparatus of any one of embodiments 46-52, wherein the offeradjustment interface further comprises an indication of an offeranalytics data set generated based on the resource offer set and atleast one of the at least one resource offer generation input data set

54. A computer-implemented method for generating a trusted resourcecharacteristic data set based on at least one untrusted third-partyresource characteristic data, the method comprising:

-   -   generating a trusted resource characteristic data set by        applying at least an untrusted third-party resource        characteristic data set and a distributed resource        characteristic data set from a distributed user platform to an        exception detection model,    -   wherein applying the exception detection model comprises:        -   integrating the untrusted third-party resource            characteristic data set and the distributed resource            characteristic data set from the distributed user platform;        -   identifying an offset between the untrusted third-party            resource characteristic data set and the distributed            resource characteristic data set from the distributed user            platform;        -   identifying an exception period set, comprising at least one            exception period in the untrusted third-party resource            characteristic data set, based upon a deviation in the            offset;        -   removing the exception period set from the untrusted            third-party resource characteristic data set to generate an            updated untrusted third-party resource characteristic data            set; and        -   generating the trusted resource characteristic data set            based on at least the updated untrusted third-party resource            characteristic data set.

55. The computer-implemented method of embodiment 54, whereinintegrating the untrusted third-party resource characteristic data setand the distributed resource characteristic data set comprises:

-   -   aligning the untrusted third-party resource characteristic data        set and the distributed resource characteristic data set based        on a temporal alignment.

56. The computer-implemented method of any one of embodiments 54 or 55,wherein integrating the untrusted third-party resource characteristicdata set and the distributed resource characteristic data set comprises:

-   -   aligning the untrusted third-party resource characteristic data        set and the distributed resource characteristic data set based        on a temporal alignment and a resource set identifier alignment.

57. The computer-implemented method of any one of embodiments 54-56,wherein identifying the offset between the untrusted third-partyresource characteristic data set and the distributed resourcecharacteristic data set from the distributed user platform comprises:

-   -   comparing a first characteristic of a first resource in the        untrusted third-party resource characteristic data set with the        first characteristic of the first resource in the distributed        resource characteristic data set from the distributed user        platform to identify the offset.

58. The computer-implemented method of embodiment 57, wherein the firstcharacteristic of the first resource in the untrusted third-partyresource characteristic set comprises a first average characteristic forthe first characteristic based on the untrusted third-party resourcecharacteristic set over a predefined timestamp interval, and wherein thefirst characteristic of the first resource in the distributed resourcecharacteristic data set comprises a second average for the firstcharacteristic of the first resource based on the distributed resourcecharacteristic data set associated with the predefined timestampinterval, and wherein the comparing comprises:

-   -   comparing the first average characteristic of the first resource        based on the untrusted third-party resource characteristic data        set with the second average for the first characteristic of the        first resource based on the distributed resource characteristic        data set from the distributed user platform to identify the        offset,    -   wherein the offset is associated with the predefined timestamp        interval.

59. The computer-implemented method of any one of embodiments 54-58,wherein identifying the at least one exception period in the untrustedthird-party resource characteristic data set based upon the deviation inthe offset comprises:

-   -   identifying a first timestamp at which the deviation of the        offset satisfies an exception deviation threshold;    -   identifying a second timestamp at which the deviation of the        offset does not satisfy the exception deviation threshold; and    -   generating a first exception period based on the first timestamp        and the second timestamp.

60. The computer-implemented method of any one of embodiments 54-59,wherein the untrusted third-party resource characteristic data setcomprises a third-party resource pricing data set.

61. The computer-implemented method of any one of embodiments 54-60,wherein the distributed resource characteristic data set comprises adistributed resource pricing data set.

62. The computer-implemented method of any one of embodiments 54-61,further comprising:

-   -   applying a second untrusted third-party resource characteristic        data set and the distributed resource characteristic data set        from the distributed user platform to the exception detection        model,    -   wherein applying the exception detection model comprises:        -   integrating the second untrusted third-party resource            characteristic data set and the distributed resource            characteristic data set from the distributed user platform;        -   identifying a second offset between the second untrusted            third-party resource characteristic data set and the            distributed resource characteristic data set from the            distributed user platform;        -   identifying a second exception period set, comprising at            least one exception period in the second untrusted            third-party resource characteristic data set, based upon a            second deviation in the second offset;        -   removing the second exception period set from the second            untrusted third-party resource characteristic data set to            generate an updated second untrusted third-party resource            characteristic data set;        -   comparing the updated untrusted third-party resource            characteristic data set with the updated second untrusted            third-party resource characteristic data set; and        -   wherein generating the trusted resource characteristic data            set based on at least the updated untrusted third-party            resource characteristic data set comprises:            -   generating the trusted resource characteristic data set                based on the comparison of the updated untrusted                third-party resource characteristic data set with the                updated second untrusted third-party resource                characteristic data set.

63. An apparatus for generating a trusted resource characteristic dataset based on at least one untrusted third-party resource characteristicdata, the apparatus comprising at least one processor and at least onememory comprising computer program code, the at least one memory and thecomputer program code configured to, with the at least one processor,cause the apparatus to:

-   -   generate a trusted resource characteristic data set by applying        at least an untrusted third-party resource characteristic data        set and a distributed resource characteristic data set from a        distributed user platform to an exception detection model,    -   wherein to apply the exception detection model, the computer        program code causes the apparatus to:        -   integrate the untrusted third-party resource characteristic            data set and the distributed resource characteristic data            set from the distributed user platform;        -   identify an offset between the untrusted third-party            resource characteristic data set and the distributed            resource characteristic data set from the distributed user            platform;        -   identify an exception period set, comprising at least one            exception period in the untrusted third-party resource            characteristic data set, based upon a deviation in the            offset;        -   remove the exception period set from the untrusted            third-party resource characteristic data set to generate an            updated untrusted third-party resource characteristic data            set; and        -   generate the trusted resource characteristic data set based            on the updated untrusted third-party resource characteristic            data set.

64. The apparatus of embodiment 63, wherein, to integrate the untrustedthird-party resource characteristic data set and the distributedresource characteristic data set, the computer program code cause theapparatus to:

-   -   align the untrusted third-party resource characteristic data set        and the distributed resource characteristic data set based on a        temporal alignment.

65. The apparatus of any one of embodiments 63 or 64, wherein, tointegrate the untrusted third-party resource characteristic data set andthe distributed resource characteristic data set, the computer programcode cause the apparatus to:

-   -   align the untrusted third-party resource characteristic data set        and the distributed resource characteristic data set based on a        temporal alignment and a resource set identifier alignment.

66. The apparatus of any one of embodiments 63-65, wherein, to identifythe offset between the untrusted third-party resource characteristicdata set and the distributed resource characteristic data set from thedistributed user platform, the computer program code cause the apparatusto:

-   -   compare a first characteristic of a first resource in the        untrusted third-party resource characteristic data set with the        first characteristic of the first resource in the distributed        resource characteristic data set from the distributed user        platform to identify the offset.

67. The apparatus of embodiment 66, wherein the first characteristic ofthe first resource in the untrusted third-party resource characteristicset comprises a first average characteristic for the firstcharacteristic based on the untrusted third-party resourcecharacteristic set over a predefined timestamp interval, and wherein thefirst characteristic of the first resource in the distributed resourcecharacteristic data set comprises a second average for the firstcharacteristic of the first resource based on the distributed resourcecharacteristic data set associated with the predefined timestampinterval, and wherein to compare, the computer program code cause theapparatus to:

-   -   compare the first average characteristic of the first resource        based on the untrusted third-party resource characteristic data        set with the second average for the first characteristic of the        first resource based on the distributed resource characteristic        data set from the distributed user platform to identify the        offset,    -   wherein the offset is associated with the predefined timestamp        interval.

68. The apparatus of any one of embodiments 63-67, wherein, to identifythe at least one exception period in the untrusted third-party resourcecharacteristic data set based upon the deviation in the offset, thecomputer program code cause the apparatus to:

-   -   identify a first timestamp at which the deviation of the offset        satisfies an exception deviation threshold;    -   identify a second timestamp at which the deviation of the offset        does not satisfy the exception deviation threshold; and    -   generate a first exception period based on the first timestamp        and the second timestamp.

69. The apparatus of any one of embodiments 63-68, wherein the untrustedthird-party resource characteristic data set comprises a third-partyresource pricing data set.

70. The apparatus of any one of embodiments 63-69, wherein thedistributed resource characteristic data set comprises a distributedresource pricing data set.

71. The apparatus of any one of embodiments 63-70, the at least onememory and the computer program code further configured to, with the atleast one processor, cause the apparatus to:

-   -   apply a second untrusted third-party resource characteristic        data set and the distributed resource characteristic data set        from the distributed user platform to the exception detection        model,    -   wherein to apply the exception detection model, the computer        program code cause the apparatus to:        -   integrate the second untrusted third-party resource            characteristic data set and the distributed resource            characteristic data set from the distributed user platform;        -   identify a second offset between the second untrusted            third-party resource characteristic data set and the            distributed resource characteristic data set from the            distributed user platform;        -   identify a second exception period set, comprising at least            one exception period in the second untrusted third-party            resource characteristic data set, based upon a second            deviation in the second offset;        -   remove the second exception period set from the second            untrusted third-party resource characteristic data set to            generate an updated second untrusted third-party resource            characteristic data set;        -   compare the updated untrusted third-party resource            characteristic data set with the updated second untrusted            third-party resource characteristic data set; and        -   wherein to generate the trusted resource characteristic data            set based on at least the updated untrusted third-party            resource characteristic data set, the computer program code            is configured to cause the apparatus to:            -   generate the trusted resource characteristic data set                based on the comparison of the updated untrusted                third-party resource characteristic data set with the                updated second untrusted third-party resource                characteristic data set.

72. A computer-implemented method for rendering an offer adjustmentinterface to a client device for facilitating adjustment and approvalvia an offer adjustment interface, the method comprising:

-   -   dynamically rendering an offer analysis table, the offer        analysis table comprising an indication of a received resource        offer set comprising one or more resource offer data objects,    -   wherein the offer analysis table is configured for navigating,        by an offer control user of the client device, the received        resource offer set, and    -   wherein each resource offer data object is configured for        receiving user input of an adjusted resource offer data object        in real-time;    -   dynamically rendering, in a first region non-overlapping with        the offer analysis table, a dashboard for accessing one or more        analysis interfaces, the one or more analysis interfaces        configured based on the resource offer set;    -   dynamically rendering, in a second region non-overlapping with        the offer analysis table and the dashboard, an indication of an        offer analytics data object, wherein the offer analytics data        object is based on the resource offer set;    -   in response to user input of at least one adjusted resource data        object for at least one selected resource data object:        -   identifying an adjusted resource offer set based on the            received resource offer set and the at least one adjusted            resource data object;        -   in real-time, dynamically rendering, in real-time, the at            least one adjusted resource data object to the offer            analysis table; and        -   in real-time, dynamically updating, based on the adjusted            resource offer set, the rendering of the indication of the            offer analytics data object; and    -   dynamically rendering an offer submitting component configured        for, in response to user engagement with the offer submitting        component, transmitting a completion control signal.

73. The computer-implemented method of embodiment 72, furthercomprising:

-   -   in response to the user input of the at least one adjusted        resource data object for the at least one selected resource data        object, dynamically updating the one or more analysis interfaces        based on the adjusted resource offer set.

74. The computer-implemented method of any one of embodiments 72 or 73,further comprising:

dynamically rendering an offer saving component configured for, inresponse to user engagement with the offer saving component,transmitting one or more control signals comprising the at least oneadjustment data objects.

75. An apparatus for rendering an offer adjustment interface to a clientdevice for facilitating adjustment and approval via an offer adjustmentinterface, the apparatus comprising at least one processor and at leastone memory comprising computer program code, the at least one memory andthe computer program code configured to, with the at least oneprocessor, cause the apparatus to:

-   -   render, dynamically an offer analysis table, the offer analysis        table comprising an indication of a received resource offer set        comprising one or more resource offer data objects,    -   wherein the offer analysis table is configured for navigating,        by an offer control user of the client device, the received        resource offer set, and    -   wherein each resource offer data object is configured for        receiving user input of an adjusted resource offer data object        in real-time;    -   render, dynamically, in a first region non-overlapping with the        offer analysis table, a dashboard for accessing one or more        analysis interfaces, the one or more analysis interfaces        configured based on the resource offer set;    -   render, dynamically, in a second region non-overlapping with the        offer analysis table and the dashboard, an indication of an        offer analytics data object, wherein the offer analytics data        object is based on the resource offer set;    -   in response to user input of at least one adjusted resource data        object for at least one selected resource data object:        -   identify an adjusted resource offer set based on the            received resource offer set and the at least one adjusted            resource data object;        -   render, dynamically and in real-time, the at least one            adjusted resource data object to the offer analysis table;            and        -   update, dynamically and in real-time, based on the adjusted            resource offer set, the rendering of the indication of the            offer analytics data object; and    -   render, dynamically, an offer submitting component configured        to, in response to user engagement with the offer submitting        component, transmit a completion control signal.

76. The apparatus of embodiment 75, the at least one memory and thecomputer program code further configured to, with the at least oneprocessor, cause the apparatus to:

-   -   in response to the user input of the at least one adjusted        resource data object for the at least one selected resource data        object, update, dynamically the one or more analysis interfaces        based on the adjusted resource offer set.

77. The apparatus of any one of embodiments 75 or 76, the at least onememory and the computer program code further configured to, with the atleast one processor, cause the apparatus to:

-   -   render, dynamically, an offer saving component configured for,        in response to user engagement with the offer saving component,        transmitting one or more control signals comprising the at least        one adjustment data objects.

78. A computer-implemented method for generating a resource offer set,the method comprising:

-   -   receiving a region-program identifier via one or more client        devices;    -   receiving a collection period data object associated with the        region-program identifier via the one or more client devices;    -   validating the collection period data object by comparing the        collection period data object to a valid timestamp range object;    -   retrieving at least one resource offer generation input data set        comprising at least a historical offer data set, a resource list        data set, a market intelligence data set, and a resource mapping        data set;    -   receiving a benchmark and portfolio target data set in response        to an input by an offer control user via one or more client        devices;    -   wherein the benchmark and portfolio target data set comprises at        least one collection data parameter value for a collection data        parameter associated with a region-program data object        associated with the region-program identifier;    -   generating a resource offer set by applying at least one of the        resource offer generation input data set and the benchmark and        portfolio target data set to a resource offer generation model,    -   wherein the generated resource offer set satisfies the benchmark        and portfolio target data set;    -   generating a control signal causing a renderable object        comprising an offer adjustment interface displayed at a first of        the one or more client devices and configured for updating the        resource offer set to create an adjusted resource offer set, the        offer adjustment interface comprising an indication of the        resource offer set;    -   receiving a completion control signal from the first of the one        or more client devices;    -   in response to the completion control signal, generating an        approval request control signal causing a second renderable data        object comprising an approval interface to be displayed at a        second of the one or more client devices, wherein the approval        interface comprises an indication of the adjusted resource offer        set;    -   receiving, from the second of the one or more client devices, an        offer approval control signal comprising an offer status        indicator, wherein the offer status indicator comprises an        approved status indicator or a rejected status indicator; and    -   storing the resource offer set associated with the offer status        indicator.

79. The computer-implemented method of embodiment 78, further comprising

-   -   generating a trusted resource characteristic data set by        applying at least an untrusted third-party resource        characteristic data set and a distributed resource        characteristic data set from a distributed user platform to an        exception detection model,    -   wherein applying the exception detection model comprises:        -   integrating the untrusted third-party resource            characteristic data set and the distributed resource            characteristic data set from the distributed user platform;        -   identifying an offset between the untrusted third-party            resource characteristic data set and the distributed            resource characteristic data set from the distributed user            platform;        -   identifying an exception period set, comprising at least one            exception period in the untrusted third-party resource            characteristic data set, based upon a deviation in the            offset;        -   removing the exception period set from the untrusted            third-party resource characteristic data set to generate an            updated untrusted third-party resource characteristic data            set; and        -   generating the trusted resource characteristic data set            based on the updated untrusted third-party resource            characteristic data set,    -   wherein generating the resource offer set comprises applying the        at least one resource offer generation input data set and the        trusted resource characteristic data set to the resource offer        generation model.

80. The computer-implemented method of embodiment 79, further comprising

-   -   applying a second untrusted third-party resource characteristic        data set and the distributed resource characteristic data set        from the distributed user platform to the exception detection        model,    -   wherein applying the exception detection model comprises:        -   integrating the second untrusted third-party resource            characteristic data set and the distributed resource            characteristic data set from the distributed user platform;        -   identifying a second offset between the second untrusted            third-party resource characteristic data set and the            distributed resource characteristic data set from the            distributed user platform;        -   identifying a second exception period set, comprising at            least one exception period in the second untrusted            third-party resource characteristic data set, based upon a            second deviation in the second offset;        -   removing the second exception period set from the second            untrusted third-party resource characteristic data set to            generate an updated second untrusted third-party resource            characteristic data set;        -   comparing the updated untrusted third-party resource            characteristic data set with the updated second untrusted            third-party resource characteristic data set; and        -   wherein generating the trusted resource characteristic data            set based on at least the updated untrusted third-party            resource characteristic data set comprises:            -   generating the trusted resource characteristic data set                based on the comparison of the updated untrusted                third-party resource characteristic data set with the                updated second untrusted third-party resource                characteristic data set.

81. An apparatus for generating a resource offer set, the apparatuscomprising at least one processor and at least one memory comprisingcomputer program code, the at least one memory and the computer programcode configured to, with the at least one processor, cause the apparatusto:

-   -   receive a region-program identifier via one or more client        devices;    -   receive a collection period data object associated with the        region-program identifier via the one or more client devices;    -   validate the collection period data object by comparing the        collection period data object to a valid timestamp range object;    -   retrieve at least one resource offer generation input data set        comprising at least a historical offer data set, a resource list        data set, a market intelligence data set, and a resource mapping        data set;    -   receive a benchmark and portfolio target data set in response to        an input by an offer control user via one or more client        devices;    -   wherein the benchmark and portfolio target data set comprises at        least one collection data parameter value for a collection data        parameter associated with a region-program data object        associated with the region-program identifier;    -   generate a resource offer set by applying at least one of the        resource offer generation input data set and the benchmark and        portfolio target data set to a resource offer generation model,    -   wherein the generated resource offer set satisfies the benchmark        and portfolio target data set;    -   generate a control signal causing a renderable object comprising        an offer adjustment interface displayed at a first of the one or        more client devices and configured for updating the resource        offer set to create an adjusted resource offer set, the offer        adjustment interface comprising an indication of the resource        offer set;    -   receive a completion control signal from the first of the one or        more client devices;    -   in response to the completion control signal, generate an        approval request control signal causing a second renderable data        object comprising an approval interface to be displayed at a        second of the one or more client devices, wherein the approval        interface comprises an indication of the adjusted resource offer        set;    -   receive, from the second of the one or more client devices, an        offer approval control signal comprising an offer status        indicator, wherein the offer status indicator comprises an        approved status indicator or a rejected status indicator; and    -   store the resource offer set associated with the offer status        indicator.

82. The apparatus of embodiment 81, the at least one memory and thecomputer program code further configured to, with the at least oneprocessor, cause the apparatus to:

-   -   generate a trusted resource characteristic data set by applying        at least an untrusted third-party resource characteristic data        set and a distributed resource characteristic data set from a        distributed user platform to an exception detection model,    -   wherein to apply the exception detection model, the computer        program code causes the apparatus to:        -   integrate the untrusted third-party resource characteristic            data set and the distributed resource characteristic data            set from the distributed user platform;        -   identify an offset between the untrusted third-party            resource characteristic data set and the distributed            resource characteristic data set from the distributed user            platform;        -   identify an exception period set, comprising at least one            exception period in the untrusted third-party resource            characteristic data set, based upon a deviation in the            offset;        -   remove the exception period set from the untrusted            third-party resource characteristic data set to generate an            updated untrusted third-party resource characteristic data            set; and        -   generate the trusted resource characteristic data set based            on the updated untrusted third-party resource characteristic            data set,    -   wherein to generate the resource offer set, the computer program        code cause the apparatus to apply the at least one resource        offer generation input data set and the trusted resource        characteristic data set to the resource offer generation model.

83. The apparatus of embodiment 81, the at least one memory and thecomputer program code further configured to, with the at least oneprocessor, cause the apparatus to:

-   -   apply a second untrusted third-party resource characteristic        data set and the distributed resource characteristic data set        from the distributed user platform to the exception detection        model,    -   wherein, to apply, the computer program code is configured to        cause the apparatus to:        -   integrate the second untrusted third-party resource            characteristic data set and the distributed resource            characteristic data set from the distributed user platform;        -   identify a second offset between the second untrusted            third-party resource characteristic data set and the            distributed resource characteristic data set from the            distributed user platform;        -   identify a second exception period set, comprising at least            one exception period in the second untrusted third-party            resource characteristic data set, based upon a second            deviation in the second offset;        -   remove the second exception period set from the second            untrusted third-party resource characteristic data set to            generate an updated second untrusted third-party resource            characteristic data set;        -   compare the updated untrusted third-party resource            characteristic data set with the updated second untrusted            third-party resource characteristic data set; and        -   wherein to generate the trusted resource characteristic data            set based on at least the updated untrusted third-party            resource characteristic data set, the computer program code            cause the apparatus to:            -   generate the trusted resource characteristic data set                based on the comparison of the updated untrusted                third-party resource characteristic data set with the                updated second untrusted third-party resource                characteristic data set.

84. A method for determining a predicted future demand for resources ina dynamic environment, allocating a constrained resources set in thedynamic environment, and generating, adjusting, and facilitatingapproval of a corresponding resource offer set, the method comprising:

receiving a request data object from a client device associated with auser;

receiving a tiering parameters data object;

receiving a decay parameters data object;

extracting, from the request data object, a resource request set,wherein the resource request set comprises a plurality of requestparameters;

extracting, from the tiering parameters data object, a plurality oftiering parameters;

extracting, from the decay parameters data object, a plurality of decayparameters;

assigning the channel profile to a first tier from amongst a pluralityof tiers, wherein assigning the channel profile to the first tiercomprises applying the plurality of tiering parameters and a firstrequest parameter from the plurality of request parameters to a firstmodel;

generating an adjusted resource request set associated with the user byapplying a decay curve to a second request parameter from the pluralityof request parameters, wherein the decay curve is based at least in parton the plurality of decay parameters;

determining, based on the assigned first tier and the adjusted resourcerequest set, if the channel profile satisfies each of plurality ofthreshold conditions;

in response to determining that the channel profile satisfies each ofthe plurality of threshold conditions, applying the adjusted resourcerequest set and the assigned tier to a second model to generate aresource allocation set for the channel profile;

extracting, from the request data object, a request data set, whereinthe request data set is associated with a first set of resources;

receiving a first context data object, wherein the first context dataobject is associated with one or more resource distribution channels;

retrieving a predicted channel and condition data set, whereinretrieving the predicted channel and condition data set comprisesapplying the request data set and the first context data object to afirst model;

-   -   retrieving at least one resource offer generation input data        set,    -   wherein the at least one resource offer generation input data        set comprises at least an average resource term data set based        on a portion of the predicted channel and condition data set or        the resource allocation set;    -   receiving a benchmark and portfolio target data set in response        to an input by an offer control user via one or more client        devices;    -   generating a resource offer set by applying the at least one        resource offer generation input data set and benchmark and        portfolio target data set to a resource offer generation model,        wherein the generated resource offer set satisfies the benchmark        and portfolio target data set;    -   generating a control signal causing a renderable object        comprising an offer adjustment interface displayed at a first of        the one or more client devices and configured for updating the        resource offer set to create an adjusted resource offer set, the        offer adjustment interface comprising an indication of the        resource offer set;    -   receiving a completion control signal from the first of the one        or more client devices;    -   in response to the completion control signal, generating an        approval request control signal causing a second renderable data        object comprising an approval interface to be displayed at a        second of the one or more client devices, wherein the approval        interface comprises an indication of the adjusted resource offer        set;    -   receiving, from the second of the one or more client devices, an        offer approval control signal comprising an offer status        indicator; and    -   storing the adjusted resource offer set associated with the        offer status indicator.

85. The method of embodiment 84, wherein the benchmark and portfoliotarget data set includes a distribution time delay input parameter, andthe method further comprising:

-   -   obtaining a decay parameters data object associated with a decay        curve; and    -   adjusting the average resource term data set based on the        distribution time delay input parameter and the decay curve.

86. An apparatus for determining a predicted future demand for resourcesin a dynamic environment, allocating a constrained resources set in thedynamic environment, and generating, adjusting, and facilitatingapproval of a corresponding resource offer set, the apparatus comprisingat least one processor and at least one memory comprising computerprogram code, the at least one memory and the computer program codeconfigured to, with the at least one processor, cause the apparatus to:

receive a request data object from a client device associated with auser;

receive a tiering parameters data object;

receive a decay parameters data object;

extract, from the request data object, a resource request set, whereinthe resource request set comprises a plurality of request parameters;

extract, from the tiering parameters data object, a plurality of tieringparameters;

extract, from the decay parameters data object, a plurality of decayparameters;

assign the channel profile to a first tier from amongst a plurality oftiers, wherein assigning the channel profile to the first tier comprisesapplying the plurality of tiering parameters and a first requestparameter from the plurality of request parameters to a first model;

generate an adjusted resource request set associated with the user byapplying a decay curve to a second request parameter from the pluralityof request parameters, wherein the decay curve is based at least in parton the plurality of decay parameters;

determine, based on the assigned first tier and the adjusted resourcerequest set, if the channel profile satisfies each of plurality ofthreshold conditions;

in response to the determination that the channel profile satisfies eachof the plurality of threshold conditions, apply the adjusted resourcerequest set and the assigned tier to a second model to generate aresource allocation set for the channel profile;

extract, from the request data object, a request data set, wherein therequest data set is associated with a first set of resources;

receive a first context data object, wherein the first context dataobject is associated with one or more resource distribution channels;

retrieve a predicted channel and condition data set, wherein retrievingthe predicted channel and condition data set comprises applying therequest data set and the first context data object to a first model;

-   -   retrieve at least one resource offer generation input data set,    -   wherein the at least one resource offer generation input data        set comprises at least an average resource term data set based        on a portion of the predicted channel and condition data set or        the resource allocation set;    -   receive a benchmark and portfolio target data set in response to        an input by an offer control user via one or more client        devices;    -   generate a resource offer set by applying the at least one        resource offer generation input data set and benchmark and        portfolio target data set to a resource offer generation model,        wherein the generated resource offer set satisfies the benchmark        and portfolio target data set;    -   generate a control signal causing a renderable object comprising        an offer adjustment interface displayed at a first of the one or        more client devices and configured for updating the resource        offer set to create an adjusted resource offer set, the offer        adjustment interface comprising an indication of the resource        offer set;    -   receive a completion control signal from the first of the one or        more client devices;    -   in response to the completion control signal, generate an        approval request control signal causing a second renderable data        object comprising an approval interface to be displayed at a        second of the one or more client devices, wherein the approval        interface comprises an indication of the adjusted resource offer        set;    -   receive, from the second of the one or more client devices, an        offer approval control signal comprising an offer status        indicator; and    -   store the adjusted resource offer set associated with the offer        status indicator.

87. The apparatus of embodiment 86, wherein the benchmark and portfoliotarget data set includes a distribution time delay input parameter, andwherein the at least one memory and the computer program code furtherconfigured to, with the at least one processor, cause the apparatus to:

-   -   obtain a decay parameters data object associated with a decay        curve; and    -   adjust the average resource term data set based on the        distribution time delay input parameter and the decay curve.

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. For example, anycombination of some or all of the subroutines and sub-processesdescribed herein may be claimed in combination or individually withoutdeparting from the scope and spirit of the present disclosure.Therefore, it is to be understood that the inventions are not to belimited to the specific embodiments disclosed and that modifications andother embodiments are intended to be included within the scope of theappended claims. Although specific terms are employed herein, they areused in a generic and descriptive sense only and not for purposes oflimitation.

What is claimed is:
 1. A computer-implemented method for generating atrusted resource characteristic data set based on at least one untrustedthird-party resource characteristic data, the method comprising:generating a trusted resource characteristic data set by applying atleast an untrusted third-party resource characteristic data set and adistributed resource characteristic data set from a distributed userplatform to an exception detection model, wherein applying the exceptiondetection model comprises: integrating the untrusted third-partyresource characteristic data set and the distributed resourcecharacteristic data set from the distributed user platform; identifyingan offset between the untrusted third-party resource characteristic dataset and the distributed resource characteristic data set from thedistributed user platform; identifying an exception period set,comprising at least one exception period in the untrusted third-partyresource characteristic data set, based upon a deviation in the offset;removing the exception period set from the untrusted third-partyresource characteristic data set to generate an updated untrustedthird-party resource characteristic data set; and generating the trustedresource characteristic data set based on at least the updated untrustedthird-party resource characteristic data set.
 2. Thecomputer-implemented method of claim 1, wherein integrating theuntrusted third-party resource characteristic data set and thedistributed resource characteristic data set comprises: aligning theuntrusted third-party resource characteristic data set and thedistributed resource characteristic data set based on a temporalalignment.
 3. The computer-implemented method of claim 1, whereinintegrating the untrusted third-party resource characteristic data setand the distributed resource characteristic data set comprises: aligningthe untrusted third-party resource characteristic data set and thedistributed resource characteristic data set based on a temporalalignment and a resource set identifier alignment.
 4. Thecomputer-implemented method of claim 1, wherein identifying the offsetbetween the untrusted third-party resource characteristic data set andthe distributed resource characteristic data set from the distributeduser platform comprises: comparing a first characteristic of a firstresource in the untrusted third-party resource characteristic data setwith the first characteristic of the first resource in the distributedresource characteristic data set from the distributed user platform toidentify the offset.
 5. The computer-implemented method of claim 4,wherein the first characteristic of the first resource in the untrustedthird-party resource characteristic set comprises a first averagecharacteristic for the first characteristic based on the untrustedthird-party resource characteristic set over a predefined timestampinterval, and wherein the first characteristic of the first resource inthe distributed resource characteristic data set comprises a secondaverage for the first characteristic of the first resource based on thedistributed resource characteristic data set associated with thepredefined timestamp interval, and wherein the comparing comprises:comparing the first average characteristic of the first resource basedon the untrusted third-party resource characteristic data set with thesecond average for the first characteristic of the first resource basedon the distributed resource characteristic data set from the distributeduser platform to identify the offset, wherein the offset is associatedwith the predefined timestamp interval.
 6. The computer-implementedmethod of claim 1, wherein identifying the at least one exception periodin the untrusted third-party resource characteristic data set based uponthe deviation in the offset comprises: identifying a first timestamp atwhich the deviation of the offset satisfies an exception deviationthreshold; identifying a second timestamp at which the deviation of theoffset does not satisfy the exception deviation threshold; andgenerating a first exception period based on the first timestamp and thesecond timestamp.
 7. The computer-implemented method of claim 1, whereinthe untrusted third-party resource characteristic data set comprises athird-party resource pricing data set.
 8. The computer-implementedmethod of claim 1, wherein the distributed resource characteristic dataset comprises a distributed resource pricing data set.
 9. Thecomputer-implemented method of claim 1, further comprising: applying asecond untrusted third-party resource characteristic data set and thedistributed resource characteristic data set from the distributed userplatform to the exception detection model, wherein applying theexception detection model comprises: integrating the second untrustedthird-party resource characteristic data set and the distributedresource characteristic data set from the distributed user platform;identifying a second offset between the second untrusted third-partyresource characteristic data set and the distributed resourcecharacteristic data set from the distributed user platform; identifyinga second exception period set, comprising at least one exception periodin the second untrusted third-party resource characteristic data set,based upon a second deviation in the second offset; removing the secondexception period set from the second untrusted third-party resourcecharacteristic data set to generate an updated second untrustedthird-party resource characteristic data set; comparing the updateduntrusted third-party resource characteristic data set with the updatedsecond untrusted third-party resource characteristic data set; andwherein generating the trusted resource characteristic data set based onat least the updated untrusted third-party resource characteristic dataset comprises: generating the trusted resource characteristic data setbased on the comparison of the updated untrusted third-party resourcecharacteristic data set with the updated second untrusted third-partyresource characteristic data set.
 10. An apparatus for generating atrusted resource characteristic data set based on at least one untrustedthird-party resource characteristic data, the apparatus comprising atleast one processor and at least one memory comprising computer programcode, the at least one memory and the computer program code configuredto, with the at least one processor, cause the apparatus to: generate atrusted resource characteristic data set by applying at least anuntrusted third-party resource characteristic data set and a distributedresource characteristic data set from a distributed user platform to anexception detection model, wherein to apply the exception detectionmodel, the computer program code causes the apparatus to: integrate theuntrusted third-party resource characteristic data set and thedistributed resource characteristic data set from the distributed userplatform; identify an offset between the untrusted third-party resourcecharacteristic data set and the distributed resource characteristic dataset from the distributed user platform; identify an exception periodset, comprising at least one exception period in the untrustedthird-party resource characteristic data set, based upon a deviation inthe offset; remove the exception period set from the untrustedthird-party resource characteristic data set to generate an updateduntrusted third-party resource characteristic data set; and generate thetrusted resource characteristic data set based on the updated untrustedthird-party resource characteristic data set.
 11. The apparatus of claim10, wherein, to integrate the untrusted third-party resourcecharacteristic data set and the distributed resource characteristic dataset, the computer program code cause the apparatus to: align theuntrusted third-party resource characteristic data set and thedistributed resource characteristic data set based on a temporalalignment.
 12. The apparatus of claim 10, wherein, to integrate theuntrusted third-party resource characteristic data set and thedistributed resource characteristic data set, the computer program codecause the apparatus to: align the untrusted third-party resourcecharacteristic data set and the distributed resource characteristic dataset based on a temporal alignment and a resource set identifieralignment.
 13. The apparatus of claim 10, wherein, to identify theoffset between the untrusted third-party resource characteristic dataset and the distributed resource characteristic data set from thedistributed user platform, the computer program code cause the apparatusto: compare a first characteristic of a first resource in the untrustedthird-party resource characteristic data set with the firstcharacteristic of the first resource in the distributed resourcecharacteristic data set from the distributed user platform to identifythe offset.
 14. The apparatus of claim 13, wherein the firstcharacteristic of the first resource in the untrusted third-partyresource characteristic set comprises a first average characteristic forthe first characteristic based on the untrusted third-party resourcecharacteristic set over a predefined timestamp interval, and wherein thefirst characteristic of the first resource in the distributed resourcecharacteristic data set comprises a second average for the firstcharacteristic of the first resource based on the distributed resourcecharacteristic data set associated with the predefined timestampinterval, and wherein to compare, the computer program code cause theapparatus to: compare the first average characteristic of the firstresource based on the untrusted third-party resource characteristic dataset with the second average for the first characteristic of the firstresource based on the distributed resource characteristic data set fromthe distributed user platform to identify the offset, wherein the offsetis associated with the predefined timestamp interval.
 15. The apparatusof claim 10, wherein, to identify the at least one exception period inthe untrusted third-party resource characteristic data set based uponthe deviation in the offset, the computer program code cause theapparatus to: identify a first timestamp at which the deviation of theoffset satisfies an exception deviation threshold; identify a secondtimestamp at which the deviation of the offset does not satisfy theexception deviation threshold; and generate a first exception periodbased on the first timestamp and the second timestamp.
 16. The apparatusof claim 10, wherein the untrusted third-party resource characteristicdata set comprises a third-party resource pricing data set.
 17. Theapparatus of claim 10, wherein the distributed resource characteristicdata set comprises a distributed resource pricing data set.
 18. Theapparatus of claim 10, the at least one memory and the computer programcode further configured to, with the at least one processor, cause theapparatus to: apply a second untrusted third-party resourcecharacteristic data set and the distributed resource characteristic dataset from the distributed user platform to the exception detection model,wherein to apply the exception detection model, the computer programcode cause the apparatus to: integrate the second untrusted third-partyresource characteristic data set and the distributed resourcecharacteristic data set from the distributed user platform; identify asecond offset between the second untrusted third-party resourcecharacteristic data set and the distributed resource characteristic dataset from the distributed user platform; identify a second exceptionperiod set, comprising at least one exception period in the seconduntrusted third-party resource characteristic data set, based upon asecond deviation in the second offset; remove the second exceptionperiod set from the second untrusted third-party resource characteristicdata set to generate an updated second untrusted third-party resourcecharacteristic data set; compare the updated untrusted third-partyresource characteristic data set with the updated second untrustedthird-party resource characteristic data set; and wherein to generatethe trusted resource characteristic data set based on at least theupdated untrusted third-party resource characteristic data set, thecomputer program code is configured to cause the apparatus to: generatethe trusted resource characteristic data set based on the comparison ofthe updated untrusted third-party resource characteristic data set withthe updated second untrusted third-party resource characteristic dataset.